FUSION OF SENSOR TECHNOLOGY AND MULTI-PHYSICS SIMULATION OF
CEMENT HYDRATION KINETICS
By
Bin (Benjamin) Zhang
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
Dissertation Advisor: Dr. Xiong (Bill) Yu
Department of Civil Engineering
Case Western Reserve University
January, 2012
CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of
Bin (Benjamin) Zhang
________________________________
Doctor of Philosophy
candidate for the ________________________________degree *.
(signed)________Dr. Xiong(Bill) Yu__________________________
(chair of the committee)
________ Dr. Xiangwu Zeng ________________________
________ Dr. Dario Gasparini________________________
________ Dr. Arthur Huckelbridge ___________________
________ Dr. Chung-Chiun Liu ______________________
________Dr. John Lewandowski _____________________
________ Dr. Edward J. Garboczi _____________________
(date) _____9/9/2011__________________
*We also certify that written approval has been obtained for any proprietary material
contained therein.
2
Table of Contents
TABLE OF CONTENT ................................................................................................................... I
LIST OF FIGURES ....................................................................................................................... IV
ACKNOWLEDGEMENT............................................................................................................. XI
ABSTRACT ................................................................................................................................. XII
ACKNOWLEDGEMENT.......................................................................................................... XIV
CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW ............................................... 1
1.1 Introduction ............................................................................................................................ 1
1.1.1 Formulation and Implementation of A Multi-Physical Simulation Model for Fresh
Concrete Hydration Kinetics ................................................................................................... 1
1.1.2 Development of Innovative Guided Radar Technologies to Characterize the Hydration
Kinetics.................................................................................................................................... 1
1.1.3 Study the Effects of Nano-Cement, Mineral Nano-Particles on The Microstructure and
Durability of Concrete ............................................................................................................. 3
1.2 Literature review .................................................................................................................... 3
1.2.1 Cement Hydration and Early Stage Concrete................................................................. 3
1.2.2 Experimental approaches for Cement Hydration Studies............................................... 7
1.2.3 Computer Simulation for Cement Hydration Studies .................................................. 14
1.2.4 Microstructure Studies for Cement and Concrete ........................................................ 15
1.3 Organization of this Dissertation .......................................................................................... 16
CHAPTER 2 MULTIPHYSICAL SIMULATIONS OF............................................................. 17
FRESH CONCRETE KINETICS ................................................................................................. 17
2.1
OVERVIEW OF RESEARCH THRUST ...................................................................... 17
2.2
ENGINEERING SIGNIFICANCE AND CURRENT STATE OF RESEARCH.......... 17
2.3
MULTIPHYSICS THEORY FOR CEMENT HYDRATION SIMULATIONS ........... 17
2.3.1 Chemical Reactions in the Cement Hydration Process ................................................ 18
2.3.2 Model Descriptions ...................................................................................................... 20
2.3.3 Results and Analyses .................................................................................................... 22
2.4 Conclusions .......................................................................................................................... 56
CHAPTER 3
DEVELOPMENT OF INNOVATIVE TECHNOLOGY FOR TRANSPORT
PROPERTIES OF EARLY STAGE CONCRETE ....................................................................... 59
3.1 OVERVIEW OF RESEARCH THRUST ............................................................................ 59
3.2 Thermal Properties Measurement by Developing Thermal TDR Technology .................. 59
3.2. 1 Theoretical Basis of Thermal Pulse Technology (TPT) .............................................. 60
3.2.3. Method of Thermo-TDR Signal Analyses .................................................................. 72
3.2.4 Improvement of Thermo-TDR Probe Design and Inversion Analyses by the FEM
Analyses ................................................................................................................................ 77
3.2.5. RESULTS AND ANALYSES .................................................................................... 90
3.3 Thermo-Tdr Probe Responses For The Soils Subjected To Freezing-Thaw Process......... 101
3.4 Conclusions ........................................................................................................................ 108
CHAPTER 4 THE EFFECTS OF NANO-SILICA FUME ON THE MICROSTRUCTURE AND
PERFORMANCE PROPERTIES OF GYPSUM MORTAR ..................................................... 110
4.1 Introduction ........................................................................................................................ 110
4.2 EXPERIMENTAL PROCEDURE ..................................................................................... 111
4.3 RESULTS ANALYSIS ...................................................................................................... 114
4.3.1 Thermal Process ......................................................................................................... 114
4.3.2 TDR Signals Analysis ................................................................................................ 115
4.3.3 Ultrasonic Pulse Velocity (UPV) Analysis ................................................................ 116
4.3.4 Compression Test Analysis ........................................................................................ 117
II
4.3.5 SEM Analysis ............................................................................................................. 118
4.4 CONCLUSIONS ................................................................................................................ 119
4.5 Summary and Future Plan .................................................................................................. 120
CHAPTER 5 THE EFFECTS OF NANO-SILICA FUME ON THE MICROSTRUCTURE AND
PERFORMANCE PROPERTIES OF PORTLAND CEMENT ................................................. 123
5.1 Introduction ........................................................................................................................ 123
5.2 EXPERIMENTAL PROCEDURE ..................................................................................... 124
5.3 EXPERIMENTS AND RESULTS ANALYSIS ................................................................ 126
5.3.1 Monitoring of Hydration InducedThermal Process .................................................... 126
5.3.2 TDR Signals Analysis ................................................................................................ 127
5.3.3 Ultrasonic Pulse Velocity (UPV) Analysis ................................................................ 129
5.3.4 Results of Compression Strength ............................................................................... 130
5.3.5 Splitting Test Analysis ............................................................................................... 132
5.3.6 Abrasion Test ............................................................................................................. 134
5.3.7 SEM Analysis ............................................................................................................. 135
CHAPTER 6 THE EFFECTS OF NANO-TITANIUM FUME ON THE MICROSTRUCTURE
AND PERFORMANCE PROPERTIES OF PORTLAND CEMENT MORTAR...................... 138
6.1 EXPERIMENTAL PROCEDURE ..................................................................................... 138
6.2 EXPERIMENTS AND RESULTS .................................................................................... 140
6.2.1 Monitoring of Hydration InducedThermal Process .................................................... 140
6.2.2 TDR signal analysis.................................................................................................... 140
6.2.3 Ultrasonic Pulse Velocity (UPV) Analysis ................................................................ 144
6.2.4 Results of Compression Strength ............................................................................... 145
6.2.5 Splitting Test Analysis ............................................................................................... 146
III
6.2.6 Abrasion Test ............................................................................................................. 147
6.3 Microstructure Study for Additive Modified Concrete ...................................................... 149
6.3.1 Testing Equipment Involved in the Microstructure Study ......................................... 150
6.4 Result Analysis ................................................................................................................... 153
6.5 Conclusions ........................................................................................................................ 160
6.6 Future Work ....................................................................................................................... 161
CHAPTER 7 CONCLUSIONS AND FUTURE WORK............................................................ 163
7.1 Conclusions ........................................................................................................................ 163
7.1.1 Multi-Physical Simulation for Concrete Hydration Kinetics ..................................... 163
7.1.2 Sensor Technologies to Characterize the Hydration Kinetics .................................... 163
7.1.3 Nano-Particles Additives on the Microstructure and Durability of Concrete ............ 164
7.2 Future Work ....................................................................................................................... 164
REFERENCE .............................................................................................................................. 166
IV
Tables of Figures
Figure 1.1 Early stage concrete crack (http://www.lotus-inc.com) .................................... 4
Figure 1.2 Heat capacity as a function of the degree of hydration(Bentz 2008; Bentz In
Press). .................................................................................................................................. 6
Figure 1.3 A typical acoustic emission signal(Chotard, Smith et al. 2003)........................ 7
Figure 1.4 Experiment set up for the acoustic emission test(Chotard, Smith et al. 2003) .. 8
Figure 1.5 X-ray tomography for scanning the concrete specimen(Chotard, Smith et al. . 9
2003) ................................................................................................................................... 9
Figure 1.6 CT (computation of tomography from X-ray images) images for cement
hydration monitoring at various mixing time(Chotard, Smith et al. 2003) ...................... 10
Figure 1.7 a) Schema of an example TDR system and output signal; b)A typical TDR
curve for soil and measurement of apparent length la (Drnevich 2001) ........................... 12
Figure 2.1 a) Concentration profiles of the C3S, H2O and CSH in the perfectly mixed
reactor model; b) Geometric profile of the single paste mutlti-physics model................. 21
Figure 2.2 a) The concentration of C3S (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot .................................................... 23
Figure 2.3 a) The concentration of H2O (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot .................................................... 24
Figure 2.4 a) The concentration of CSH (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot .................................................... 25
Figure 2.5 a) The temperature distribution (surface plot), and heat flux (arrows) after 1
sec and b) 100sec, c)1000 seconds, and d) 3D surface plot .............................................. 26
Figure 2.6 a) Temperature variations at different positions of hydration system, b) the
locations of the temperature profile demonstrated in Figure 2.6a. ................................... 28
V
Figure 2.7 a) The temperature discrepancy between cement and water, and b) heat flux at
the hydration boundary. .................................................................................................... 29
Figure 2.8 Water flux at the hydration boundary.............................................................. 29
Figure 2.9 a) Concentration of CSH in the whole hydration system, and b) the logarithmic
time axis plot ..................................................................................................................... 30
Figure 2.10 a) Concentration of C3S in the whole hydration system, and b) the
logarithmic time axis plot ................................................................................................. 31
Figure 2.11 a) Sketch map of hydration radius, and ......................................................... 32
b) The concentration of CSH along hydration radius after 1000 sec ................................ 32
Figure 2.12 a) Hydration radius increases with time, and b) the logarithmic time axis plot
........................................................................................................................................... 32
Figure 2.13 a) Concentration of CSH at the hydration boundary increases with time, and
b) The logarithmic time axis plot ...................................................................................... 33
Figure 2.14 a) FEM mesh for cement particle surrounded by water; b) CSH gel and heat
flux around cement particle .............................................................................................. 35
Figure 2.15 Hydration degree VS. time normalized by the half time of nano-cement
pastes ................................................................................................................................. 36
Figure 2.16 Hydration degree VS. time normalized by the half time of um size cement
pastes ................................................................................................................................. 37
Figure 2.17 Hydration degree VS. time normalized by the half time of cement pastes with
various particle sizes ......................................................................................................... 38
Figure 2.18 Hydration degree VS. time normalized by the half time of cement pastes with
various particle sizes ......................................................................................................... 39
VI
Figure 2.19 FEM model to study the effects of particle shape on hydrations .................. 41
Table 2.1 Half time of cement particles of different sizes and different length-to-width
ratio ................................................................................................................................... 42
Figure 2.20 a) C3S concentration after 100 seconds for the cement particle with a width
of 0.5mm and different length-to-height ratios. b) C3S concentration after 100 seconds
for the cement particle with a width of 1mm and different length-to-height ratios. c) C3S
concentration after 100 seconds for the cement particle with a width of 2 mm and
different length-to-height ratios ........................................................................................ 44
Figure 2.21 The half time of cement particles of different width and shape .................... 45
Figure 2.22 the normalized half time (by that of particle with infinite length-to-width
ratio) for cement particles of different width and shapes ................................................. 46
Figure 2.23 Relationship between half time and width of cement particle (of infinite
length) ............................................................................................................................... 46
Figure 2.24 Relationship between normalized half time and length-to-width ratio ......... 47
Figure 2.25 C3S concentration of the cement pastes of both rectangular shape and circle
shape after 50sec. (left) and 100sec(right) ........................................................................ 48
Figure 2.26 the half time of various cement pastes of both rectangular shape and circle
shape ................................................................................................................................. 48
Figure 2.27 Relationship between half time and radius of cement particle ...................... 51
Figure 2.28 Mesh grids of the Finite Element Model ....................................................... 53
Figure 2.29 a) Half time of cement pastes with various particle sizes, and b) the
logarithmic time axis plot. ................................................................................................ 53
VII
Figure 2.30 Temperature distribution(surface plot), concentration of CSH(contour), and
heat flux(arrows) after a) 1 second; b) 100 seconds; c) 500 seconds; d) 1000 seconds. .. 54
Figure 2.31 Percolation structure at the interface of unhydrated cement ......................... 55
Figure 3.1 Schematic of thermal pulse technology (one dimensional heat transfer with no
thermal exchange in the vertical direction) ....................................................................... 60
Figure 3.2 illustration of the source pulse and temperature responses ............................. 61
Figure 3.3 a) Schematic design of the thermal-TDR probe; b) photos of the fabricated
thermo-TDR probe ............................................................................................................ 65
Figure 3.4 Influence of water contents on TDR signals measured by the thermo-TDR
probe: a) sand and b) clay ................................................................................................ 66
Figure 3.5 a) Experiment set up for calibrating the built-in thermocouples; b) Results of
calibration ......................................................................................................................... 67
Figure 3.6 Photo showing the installation of thermo-TDR probe in clay ......................... 69
Figure 3.7 An example of measured thermal pulse response in clay................................ 69
Figure 3.8 Effects of thermal pulse duration on the thermal responses measured in sand of
8% water contents: a) 10 seconds, b) 30 seconds, c) 1 minute (sensor A: thermocouple in
center rod, sensor B and C: thermocouple in side rods) ................................................... 70
Figure 3.9 The influence of soil physical properties on the measured thermal pulse
responses for a) sand; b) clay ............................................................................................ 72
Figure 3.10 Two different tangent line methods to determine the second reflection from
TDR signal (Yu 2003) ...................................................................................................... 73
Figure 3.11 Sensitivity analysis of thermal responses to a) the thermal diffusivities
(alpha); b) rod to rod distance ........................................................................................... 76
VIII
Figure 3.12 Results of inversion analyses of thermal response signal in dry clay ........... 77
Figure3. 13 Schematic of Finite Element Model .............................................................. 79
Figure 3.14 Influence of probe length on the distribution of temperature field and
direction of heat flow for various probe lengths a) 40 mm; b) 80 mm; and c) 200 mm
(unit scale of geometry: m) ............................................................................................... 81
Figure 3.15 Temperature responses for various probe lengths (40 mm, 80 mm and 200
mm) ................................................................................................................................... 82
Figure 3.16 The influence of thermal boundary conditions on the temperature field
distribution and heat flux a) Thermal insulation; b) hc=11W/(m2*K); c) hc=50W/(m2*K);
d) constant surface temperature (275K) ............................................................................ 86
Figure 3.17 The influence of thermal boundary conditions on the thermal responses
(thermal insulation is close to low flux surface boundary) ............................................... 86
Figure 3.18 a) correction factor for 40mm probe under various testing conditions; b)
example of corrected thermal response process................................................................ 89
Figure 3.19 Example of inversion analyses results on corrected data .............................. 90
Figure 3.20 Calibration of the dielectric constant by the thermo-TDR probe for a) sand,
and b) clay ......................................................................................................................... 93
Figure 3.21 Example results of inverse analysis on the thermal responses data from a) dry
sand; b) sand with 8% water content; c) clay of 10% water content, and d) clay of 15%
water content ..................................................................................................................... 96
TABLE 3.1 Results of thermal conductivity and volumetric heat capacity for sand ....... 97
TABLE 3.2 Results of dielectric constant, thermal conductivity and volumetric heat
capacity for clay samples .................................................................................................. 97
IX
Figure 3.22 Arrangement of data into the model format for sand and clay .................... 100
Figure 3.23 Results of thermal capacity normalized by the soil dry density versus water
content for Clay............................................................................................................... 101
Figure 3.24 TDR signals for clay during freezing-thawing process ............................... 103
Figure 3.25 Temperature curve and freezing degree of the clay during freezing process
......................................................................................................................................... 104
Figure 3.26 Dielectric constant (Ka) and electric conductivity (Ecb) during the freezing
process............................................................................................................................. 104
Figure 3.27 Dielectric constant (Ka) and electric conductivity (Ecb) during the freezing
process............................................................................................................................. 105
Figure 3.28 Freezing Degree during the freezing-thaw process ..................................... 106
Figure 3.29 Environment and sensor temperature during the freezing-thaw process.... 106
Figure 3.30 Thermal Conductivity of the clay during the freezing-thaw process .......... 107
Figure 3.31 Thermal Conductivity of the clay during the freezing-thaw process .......... 108
Figure 4.1 a) Fine sand used for the cement mixture; b) SEM micrograph of the finesand
......................................................................................................................................... 111
Figure 4.2 SEM micrograms of the CaSiO4 at different magnitude levels .................... 112
Figure 4.3 Peak Identification Results of the Cement .................................................... 112
Figure 4.4 Schematic Figure of TDR and temperature monitoring system .................... 114
Figure 4.5 Temperature process during the hydration process ....................................... 115
Figure 4.6 TDR signals of Nano-SiO2 mortar specimen ................................................ 116
Figure 4.7 TDR signals of plain mortar specimen .......................................................... 116
Figure 4.8 UPV of Nano-SiO2 mortar and plain mortar ................................................ 117
X
Figure 4.9 SEM micrograms of the Nano-SiO2 mortar .................................................. 118
Figure 4.10 SEM micrograms of plain mortar ................................................................ 119
Figure 5.1 a) Fine sand used for the cement mixture; b) SEM micrograph of the finesand
......................................................................................................................................... 124
Figure 5.2 SEM image of the nano-SiO2 particles at different magnifications .............. 125
Figure 5.3 Schematic Figure of TDR and temperature monitoring system .................... 126
Figure 5.4 Temperature process during the hydration process ....................................... 127
Figure 5.5 TDR signals of plain mortar specimen (at 1, 11, 21, 151, 201, 301 and 1007
minutes after curing) ....................................................................................................... 128
Figure 5.6 TDR signals of 2% Nano-SiO2 mortar specimen (at 1, 11, 21, 151, 201, 301
and 1007 minutes after curing) ....................................................................................... 128
Figure 5.7 TDR signals of 5% Nano-SiO2 mortar specimen (at 1, 11, 21, 151, 201, 301
and 1007 minutes after curing) ....................................................................................... 129
Figure 5.8 UPV of Nano-SiO2 mortar and plain mortar ................................................ 130
Figure 5.9 Compressive strengths of Nano-SiO2 mortar and plain mortar .................... 131
Figure 5.10 Compressive strengths versus UPV of Nano-SiO2 mortar and plain mortar
......................................................................................................................................... 132
Figure 5.11 UPV of Nano-SiO2 mortar and plain mortar .............................................. 133
Figure 5.12 Splitting strengths versus UPV of Nano-SiO2 mortar and plain mortar ..... 133
Figure 5.13 Abrasion percentages using the lost weight method ................................... 135
Figure 5.14 SEM micrograms of a) plain mortar, b) 2% nano mortar,........................... 136
c) and d) 5% nano mortar................................................................................................ 136
Figure 6.1 TEM image for the nano TiO2 particles (http://nanoamor.com) ................... 138
XI
Figure 6.2 Temperature monitoring during hydration process ....................................... 140
Figure 6.3 TDR signals of plain concrete mortar specimen ........................................... 141
Figure 6.4 TDR signals of Nano-TiO2 0.5% mortar specimen ...................................... 142
Figure 6.5 TDR signals of Nano-TiO2 1% mortar specimen ......................................... 142
Figure 6.6 TDR signals of plain mortar and nano particles modified mortar at different
hydration stages .............................................................................................................. 143
Figure 6.7 An example of the ultrasonic signal for the cylinda concrete specimen test. 144
Figure 6.8 UPV of Nano-TiO2 mortar and plain mortar ................................................ 145
Figure 6.9 Compressive Strength of the Nano-TiO2 mortar and plain mortar at different
hydration stages .............................................................................................................. 146
Figure 6.10 Splitting Strength of the Nano-TiO2 mortar and plain mortar at different
hydration stages .............................................................................................................. 147
Figure 6.11 Photo of equipment for the abrasion test ..................................................... 148
Table 0.1 ......................................................................................................................... 148
Figure 6.11 Abrasion Percentage of the Nano-TiO2 mortar and plain mortar at different
hydration stages .............................................................................................................. 149
Figure
6.12
Field-Emission
Gun
Scanning
Electron
Microscope
Hitachi
S4500(“http://dmseg5.case.edu/Groups/ernst/scsam.html”) .......................................... 150
Figure 6.13 Dual Beam Focused Ion Beam System Fei Xt Nova Nanolab
200(“http://dmseg5.case.edu/Groups/ernst/scsam.html”)............................................... 151
Figure 6.14 SEM image and chemical component contour of nano particles modified
concrete (Oxygen and Sodium) ...................................................................................... 154
XII
Figure 6.15 SEM image and chemical component contour of nano particles modified
concrete (Magnesium, Aluminum and Silicon) .............................................................. 155
Figure 6.16 SEM image and chemical component contour of nano particles modified
concrete (Sulfur, Potassium and Calcium)...................................................................... 156
Figure 6.17 SEM image and chemical component contour of nano particles modified
concrete (Titanium and Iron) .......................................................................................... 157
Figure 6.18 SEM image for crack zone of nano particles modified concrete, Figure b is
the zoom in Figure for the area in the white block in Figure a ....................................... 158
Figure 6.19 SEM image for crack zone of nano particles modified concrete................ 159
Figure 6.20 SEM image for surface conformation of nano particles modified concrete 160
XIII
ACKNOWLEDGEMENT
First of all, I would like to express my sincere appreciation to my advisor Dr. Bill Yu for
his instructions, guidance and support in both the scientific research and my personal life. Thanks
to him, I had the opportunities of participating in many inspiring research projects during my
studies. Without his insights I would not be where I am today.
I would like to thank the following for serving on my committee and for their
guidance and help, Dr. Xiangwu Zeng, Dr. Adel Saada,
Dr. Dario Gasparini, Dr. Arthur
Huckelbridge, Dr. Chung-Chiun Liu, Dr. John Lewandowski and Dr. Edward J. Garboczi. I also
thank Dr. Robert Mullen and Dr. Steven A. Hauck for serving on my PhD qualification
committee.
My thesis project was sponsored by National Science Foundation. I would also like to
thank their support of my research project. Appreciations are also given to Dr. Edward J.
Garboczi, and Dale P. Bentz for sharing information and resources and especially holding the
ACBM/NIST computer modeling workshop. I would also like to thank Dr. Frank Rausche and
Garland Lirkins from GRL/Pile Dynamics, Inc. for bringing me the interesting research topic and
generous financial support during my first year’s studies at Case Western Reserve University.
I would also like to thank Nancy A. Longo who is always available to help me with
everything. I sincerely appreciate all the help my fellow graduate students offered to me.
Appreciations are also given to my parents, and all my friends!
XIV
Fusion of Sensor Technology and Multi-Physics Simulation of
Cement Hydration Kinetics
Abstract
by
BIN (BENJAMIN) ZHANG
This dissertation explored the development of several innovative guided radar
technologies to characterize the hydration kinetics of cement based materials. A
broadband time domain dielectric spectroscopy (TDS) technology was developed to
study the interactions of concrete components in different scales. The signal analyses
based on time domain interpretation were found to correspond to the low gigahertz and
low kilohertz respectively. An innovative Thermo-TDR sensor was developed to measure
the physical, thermal and other transport properties of construction materials including
concrete.
This sensor integrates the conventional TDR probe with a heat pulse
measurement system. It can be used to collect both the TDR signals and thermal signals
at the same time. With the assistance of these sensors, the effects of nano-cement,
mineral nano-particles on the microstructure and durability of concrete was also studied
in this dissertation, and experimental studies were carried out to evaluate the effects of
mineral nano-particles on the microstructure of concrete.
XV
Besides the laboratory experiment, a multi-physics numerical model was
developed to predict the development of cement paste hydration. The chemical reaction
theory, heat transfer theory and diffusion theory were coupled in this model. The
simulation results were validated based on field test phenomenon and experiential
equations, and promising results were achieved. Besides predicting the development of
the hydration process, this model also proposed a microstructure based approach to relate
the chemical reactions to the strength of cement paste. Current results showed that this
numerical model can help predict the early stage concrete behaviors.
XVI
CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW
1.1 INTRODUCTION
1.1.1 Formulation and Implementation of A Multi-Physical Simulation Model for
Fresh Concrete Hydration Kinetics
A multi-physics numerical model was developed to predict the development of
cement paste hydration. The chemical reaction theory, heat transfer theory and diffusion
theory were coupled in this model. The simulation results were validated based on field
test phenomenon and experiential equations, and promising results were achieved.
Besides predicting the development of the hydration process, this model also proposed a
microstructure based approach to relate the chemical reactions to the strength of cement
paste. Current results showed that this numerical model can help predict early stage
concrete behaviors. The existing diffusion theory predicts that the size effects of cement
particles on the chemical hydration can be accounted for by normalizing the reaction time
with characteristic time. The microstructure imaging unveils the nanoporous structure on
the surface of unhydrated cement. We suspect that the diffusion theory, which was based
on continuous model assumption and ignores the particle-tunnel interactions, might need
to be revisited.
1.1.2 Development of Innovative Guided Radar Technologies to Characterize the
Hydration Kinetics
The project explored the development of a broadband time domain
dielectrospectroscopy (TDS) technology to study the interactions of concrete components
in different scales. We explored different approaches for dielectric spectra determination.
One approach is using a model-based inversion analyses. We refined the models for
electromagnetic wave propagation in guided electromagnetic wave radar (Time Domain
Reflectometry) systems and applied inversion analysis to estimate the dielectric spectra
of concrete. The other approach we explored was a non model-based technology for
dielectric spectra determination. By using these technologies, TDS will determine the
dielectric spectra from kilohertz to gigahertz range. Preliminary testing data validated the
performance of these systems. The signal analyses based on time domain interpretation
were found to correspond to the low gigahertz and low kilohertz respectively.
We have also developed an innovative Thermo-TDR sensor to measure the
physical, thermal and other transport properties of geomaterials including concrete. This
sensor integrates the conventional TDR probe with a heat pulse measurement system. It
can be used to collect both the TDR signals and thermal signals at the same time. From
these signals, both the physical properties and thermal properties can be determined.
This technology features the advantages of being multifunctional, sensitive, inexpensive,
rugged and easily deployed. The performance was evaluated in laboratory experiments
and demonstrated promising results. This technology can be used to study a variety of
thermal and transport phenomena in concrete. The refined system will help investigate
the thermal and transport properties of concrete in relationship to its durability.
The project also developed a preliminary framework using advanced ultrasonic
methods to characterize the pore structures in concrete. The method fusions the advanced
wave scattering model and inversion analyses model to study the pore structure of
concrete at different scales (such as the entrained air voids in mm range and the intrinsic
air voids in um range). The pore volume of concrete and its size distribution were found
2
to have a significant effect on the frequency dependent ultrasonic wave attenuation. By
using a general ultrasonic scattering model, we explored the use of an inversion analyses
procedure, which is based on the attenuation of ultrasonic wave, to estimate the volume
and the size distribution of air voids in cement paste.
Preliminary results are
encouraging. The information on pore characteristics will help to understand the
behaviors of concrete in relationship to its durability.
1.1.3 Study the Effects of Nano-Cement, Mineral Nano-Particles on The
Microstructure and Durability of Concrete
With the assistance of simulation and characterization tools developed from this
project, experimental studies were carried out to evaluate the effects of mineral nanoparticles on the microstructure of concrete. The final goal was set to understand such
interactions and durability mechanism at the lowest structural levels. It was found the
addition of nano-particles changed the hydration rate as well as the microstructure of
cement. However, the conglomerate of nano-particles was observed in the cement matrix
which requires further improvement in the dispersion procedures.
1.2 LITERATURE REVIEW
1.2.1 Cement Hydration and Early Stage Concrete
Concrete structures are subjected to the risk of cracking during the early stage
hardening process. Several possible origins are blamed for cracking damage, such as the
temperature gradient, the autogenous cracking, drying shrinkage gradient and restrained
3
strains. Most of these are linked to the cement hydration development (Buffo-Lacarriere
et al 2007, Ye et al. 2003).
Figure 1.1 Early stage concrete crack (http://www.lotus-inc.com)
Gravity force and the local dry environment will devastate the structure of the
cement paste, mortar or concrete immediately after the placement. The freshly cast
materials could be regarded as a water filled porous media. Then the solid particles
started to settle and the corresponding water rises to the top of the structure(Bentz 2008).
At the very start of the cement hydration process, some small hydration will take
place. In fact, the influence of the small hydration served to accelerate the material
transition from suspension particles in a solution to a granular porous material. However,
4
at the very early stages of the cement curing process, the reduction of the capillary pore
sizes could be overwhelmed by the effects due to setting and local particle
rearrangement(D'Angelo, Plona et al. 1995; Bentz, Geiker et al. 2001; .W. Bullard 2006;
Bentz 2008).
Hydration is in fact a kind of physical, thermal and moisture effect. The most
significant influence factors for this effect are water content and cement particle size
distribution. Cement setting is a kind of percolation process where particles are connected
together and connecting boundaries are formed(Jiang, Mutin et al. 1995; Princigallo, Lura
et al. 2003; Bentz 2008; Lin and Meyer 2009).
Thermal effect is usually blamed for the early stage concrete cracking if
appropriate curing process is applied. During the cement hydration, the concrete will
expand first and then cooling down. The concrete may crack if the cooled period is too
rapid. As shown in Figure 1.2, the cement heat capacity is a strong function of water
cement ratio and curing conditions.
5
Figure 1.2 Heat capacity as a function of the degree of hydration(Bentz 2008; Bentz In
Press).
Hydration heat and environment factors also influence the early age concrete
properties. Chemical shrinkage, self-desiccation and internal relative humidity also
played important roles in the cement hydration process(Jensen 1990; Bentz 1997;
Andrade, Sarr et al. 1999; Jensen, Hansen et al. 1999; Bentz, Jensen et al. 2001; Grasley,
Lange et al. 2006).
Autogenous shrinkage can induce the early age concrete cracking, so it’s
meaningful to measure the amount of antygenous shrinkage to predict the concrete
cracking. This measurement is very difficult due to the confounding influence of water
ingress(P.E. Roelfstra 1994; K. Maekawa 1999; Bentz 2008).
6
1.2.2 Experimental approaches for Cement Hydration Studies
Acoustic emission technique is a widely accepted solution for the cement
hydration monitoring. This method was originally applied to monitor the mechanical flaw
and failures for engineering materials, especially the cracking process inside of a
structure material. The advantage of this technique is that it is able to scan a large area of
structure with a very limited number of sensors. For the cement curing process, the
acoustic emission method could be applied to record the elastic waves emitted during the
chemical reactions and the physical changes(Chotard, Smith et al. 2003).
Figure 1.3 A typical acoustic emission signal(Chotard, Smith et al. 2003)
7
Figure 1.4 Experiment set up for the acoustic emission test(Chotard, Smith et al. 2003)
X-ray tomography could also be applied to measure the materials mechanical
properties during the cement hydration process, especially to record the density changes
of the water filled porous media. CT (computation of tomography from X-ray images)
images could provide information on the evolution of the cement mixture at the
microscopic scale(Chotard, Smith et al. 2003).
8
Figure 1.5 X-ray tomography for scanning the concrete specimen(Chotard, Smith et al.
2003)
9
Figure 1.6 CT (computation of tomography from X-ray images) images for cement
hydration monitoring at various mixing time(Chotard, Smith et al. 2003)
Time Domain Reflectometry (TDR) is a guided radar technology that was initially
used by electrical engineers to locate cable breakages. The application was extended to
measure soil water content due to the pioneering work by Topp et al. (1980). In civil
engineering, TDR has become an established technology for soil water content
measurement (O’Connor and Dowding 1999, Benson 2006, ASTM D6565 and ASTM
D6780). It features the advantages of being rugged, accurate and automatic.
The configuration of a typical TDR system is shown in Figure 1.7. The system
generally consists of a TDR device (including an electrical pulse generator and a
sampler), a connection cable, and a measurement probe (Figure 1.7a). TDR works by
sending a fast rising step pulse or impulse to the measurement probe and measuring the
10
reflections due to the change of material dielectric permittivity. Due to the large contrast
between the dielectric constant of water (around 81) and those of the air (1) or soil solids
(the dielectric constant for dry solids is typically between 3-7), the bulk dielectric
constants of soils are very sensitive to the water content. The large contrast in the
dielectric properties of air and soil causes one reflection when the electrical signal enters
the soil from the air; another reflection takes place when the electrical signal arrives at
the end of the measurement probe (Figure 1.7b). In displaying a TDR signal, the time
scale, t, is typically displaced as a round trip distance using Equation 1:
La =
ct
2
(1)
where La is typically called the apparent length, c is the speed of electromagnetic wave in
the vacuum (3.0×108 m/s), t is the time scale.
From the apparent length, La, displayed on TDR signal (Figure 1.7b), the round
trip time required for an electrical pulse to travel through the measurement probe can be
determined as t =
2 La
.
c
11
(a)
(b)
Figure 1.7 a) Schema of an example TDR system and output signal; b)A typical TDR
curve for soil and measurement of apparent length la (Drnevich 2001)
The velocity of the electromagnetic wave traveling in the testing material can then
be calculated by Equation (2).
v=
2L
2L
L
=
=
c
t
2 La / c La
12
(2)
where v is the velocity of an electromagnetic wave traveling in the material, La is the
apparent length from the displayed TDR signal, L is the physical length of the TDR
sensor section; t is the time difference between the two reflections that occur at the
interfaces of material layers.
The velocity of the electric signal is inversely proportional to the square root of
the dielectric constant, Ka, (Ramo et al. 1994):
v=
c
Ka
(3)
Combining Equations (2) and (3), the dielectric constant of a material can be
calculated by
2
c L 
Ka =   =  a 
v  L 
2
(4)
The dielectric constant, Ka, measured by TDR is typically called “apparent
dielectric constant” to reflect the fact that it does not consider the frequency-dependency
of the dielectric permittivity (Topp et al. 1980).
Dr I. L. Al-Qadi etc. applied the low frequency TDR to detect the curing status of
Portland concrete. In his experiment, the imaginary part of the relative permittivity was
used for the measurement. Both time domain and frequency domain showed consistent
results(Al-Qadi, Riad et al. 1997). Combined time domain reflectometry and AC-impedance
13
spectroscopy were applied to perform the non-destructive evaluation of the fresh cement based
materials(Shui, Zhang et al.).
1.2.3 Computer Simulation for Cement Hydration Studies
Several numerical models have been developed to investigate the microstructure
of concrete or to predict the cement hydration process. Promising results were achieved
with these models. Some of the most commonly used models include:
NIST-Model by Bentz and Garboczi (1998): It is a microstructure-based model
from digital images. In this model, a digital image of cement paste sample is subdivided
into elements that are presented by pixels. Each pixel has the information about the
position in the system and the chemical composition. This model can provide high
reliable simulations. However, the model simulation requires a huge consumption of
CPU-time and a lot of memory storage due to the large number of pixels (Garboczi and
Bentz 1998, Bentz et al. 1999; (Feng, Garboczi et al. 2004; Erdogan, Quiroga et al.
2006).
Jennings and Johnson Model (1998): This model was based on the particle
approach. In this model, the hydrating cement particles are denoted as expanding spheres
and a random distribution was considered as the initial state. The overlap volume is
smeared out around the outer shells at the contact zone where hydrating cement particles
meet. This model can be employed to predict the development of the microstructures and
the volume situation under various hardening conditions (Koenders et 1997, Roy 1993).
14
HYMOSTRUC Models: The basic HYMOSTRUC model employed factors
including the particle size distribution and the chemical composition of the cement, the
water cement ratio and the reaction temperature to calculate the hydration curves. The
difference between this model from most other models lies in the fact that the
HYMOSTRUC model could explicitly model the effect of the physical interactions
between hydrating cements on the rate of hydration process (Garbovzi and Bentz 1998,
Roy 1993, Moss et al. 1996, Princigallo et al. 2003, ven Breugel 1995).
1.2.4 Microstructure Studies for Cement and Concrete
With the flourishing development of nano technology, the use of nano-particles,
such as nano silicate fume and titanium dioxide, has received particular attention as
potential additives in cement based materials. Various technologies, such as differential
thermal analysis, Helium inflow, X-ray diffraction, scanning electron microscopy,
nuclear magnetic resonace were used to study the influences of these nano particles on
cement based materials (Qing et al. 2007, Sanchez and Ince 2009, Senff et al. 2009).
Experimental results showed positive effects of the nano particles on modifying the
mechanical properties of the hydrated materials and also decreased the hydration
durations (Jennings 2000, Jo 2007, Li et al. 2004).
Calcium sulphate cement is widely used in pavement recovery construction,
buildings and medical industries. The calcium sulphate cement carries the advantages that
it has a much shorter hydration time than the traditional Portland cement. It could be cast
into various shapes after the hydration reaction, and mixed with some polymers, it could
serve as a bone repair cement(Hand 1994; Singh and Middendorf 2007). Therefore, it is
15
very interesting to study the influences of nano particles on the calcium sulfate cement to
further improve the performance and functionality of this material.
1.3 ORGANIZATION OF THIS DISSERTATION
This research focused on the cement hydration development, which is responsible
for the long terrn performance and durability of the concrete structures. Chapter 2
presented the computer simulation approach to study the cement hydration process.
Chapter 3 talked about the sensor technology for cement hydration monitoring, and
meanwhile the measured data could provide the input iformation for the computer
simulation. Chapter 4 to chapter 6 investigated the nano engineered concrete to improve
the performance and durability. The final goal of this research is to build the high
performance and durable concrete structure.
16
CHAPTER 2 MULTIPHYSICAL SIMULATIONS OF
FRESH CONCRETE KINETICS
2.1 OVERVIEW OF RESEARCH THRUST
This research continues the previous research effort that aims at simulating the
evolution of multi-scale concrete behaviors starting from the fundamental chemical
reactions. A multi-physics based simulation approach is formulated, which couples the
chemical reaction theory, chemical diffusion theory, and thermal transport theory. This
provides a way to accurately predict the progress of the chemical reactions at the cement
particles level. A few approaches were investigated to establish the transfer functions
between microscopic hydration to the macroscopic mechanical behaviors.
2.2 ENGINEERING SIGNIFICANCE AND CURRENT STATE OF RESEARCH
In this research, we focus on developing a multi-physics simulation model to
predict the development of cement paste hydration.
This model presents holistic
simulations by coupling the chemical reaction theory, heat transfer theory and diffusion
theory.
One key issue we try to resolve is to develop the linkage between the
microscopic properties and macroscopic behaviors.
2.3 MULTIPHYSICS THEORY FOR CEMENT HYDRATION SIMULATIONS
The multi-physics process of cement hydrations couples the chemical reaction
theory, heat transfer theory and diffusion theory. These processes are correlated by mass
balance, temperature and chemical reaction rate constants. The commercial software
17
package COMSAL was applied to serve as the solver to the three coupled differential
equations. The basic equations for the multi-physics employed in this simulation are
briefly described below.
2.3.1 Chemical Reactions in the Cement Hydration Process
Basically there are four major chemical reactions that occurred during the
hydration process of the cement pastes. The reaction formulas are listed below.
I.
II.
2(3Cao∙SiO2)+6H2O=3CaO∙2SiO2∙3H2O+3Ca(OH)2
2(2CaO∙SiO2)+4H2O=3CaO∙SiO2∙3H2O+Ca(OH)2
III.
3CaO∙Al2O3+6H2O=3CaO∙Al2O3∙6H2O
IV.
4CaO∙Al2O3∙Fe2O3+7H2O=3CaO∙Al2O3∙6H2O+CaO∙Fe2O3∙H2O
The species mass balances are given by:
d (ciVr )
= Vr Ri
dt
(1)
which takes into account the effect of the volume change. In Equation 1, ci is the species
molar concentration (mol/m3), Vr denotes the reactor volume (m3), and Ri is the species
rate expression (mol/(m3·s)).
The heat released during the chemical reactions is given by:
Q = −Vr ∑ H j rj
j
18
(2)
where H j is the enthalpy of reaction (J/(mol K)), and rj the reaction rate (mol/(m3·s)), Q
is the heat due to chemical reaction (J/s).
In addition to the concentration dependence, the temperature dependence of
reaction rates can be included by using the predefined Arrhenius expressions for the rate
constants:
k = AT n exp(
E
)
Rg T
(3)
A denotes the frequency factor, n the temperature exponent, E the activation energy
(J/mol) and Rg the gas constant, 8.314 J/(mol·K).
Heat Transfer Equation
δ ts ρ C p
∂T
T ∂ρ ∂p
|
+ ∇ ( −k ∇T )= Q − ρ C p u∇T + τ : S −
∂t
ρ ∂T p ∂t
(4)
1
(∇u + (∇u )T )
2
(5)
2
τ= η[2 S − (∇u ) I ]
3
(6)
S=
where δ ts is a time-scaling coefficient (dimensionless),
ρ is the density (kg/m3), Cp is the
specific heat capacity at constant pressure (J/(kg·K)), T is absolute temperature (K), k is
the thermal conductivity (W/(m·K)), u is the velocity vector (m/s), p is pressure (Pa), S
19
is the strain rate tensor (1/s), τ is the viscous stress tensor (Pa), viscosity η (Pa·s) and I
denotes the identity tensor, Q contains heat sources other than viscous heating (W/m3).
Diffusion Equation
δ ts
∂c
+ ∇( − D∇c ) = R
∂t
(7)
where c is the concentration, D is the diffusion coefficient, and R is a reaction rate. The
diffusion process can be anisotropic, in which case D is a tensor (COMSOL 2007).
2.3.2 Model Descriptions
The simulation was first implemented on the following chemical reaction:
2(3Cao∙SiO2)+6H2O=3CaO∙2SiO2∙3H2O+3Ca(OH)2, which is one of the most common
types of chemical reaction in cement. Using commonly used notation for cement
chemistry, the hydration reaction is given as 2C3S+6H20=3CSH+3COH.
The reaction rate is calculated using the equation r =kf_1*c_C3S^2*c_H2O^6,
kf_1= AT n exp(
d (cC 3 S )
d ( cH 2 O )
d (cCSH )
d (cCOH )
E
= −2r ,
= −6r ,
=r,
= 3r where
).
dt
dt
dt
dt
Rg T
.
A=1e-5, n=0, E=100J/mol.
Figure 2.1a gave the concentration profile of the C3S, H2O and CSH when the
chemical reaction occurred in the perfectly mixed reactor model. A two dimensional
model was employed in this simulation, and the geometric profile was shown in Figure
2.1b. The total calculation area is of square shape with a length of 0.02m, and the cement
20
paste is of round shape with a diameter of 0.005m. A total of 1446 Lagrange quadratic
elements were involved in the simulation with a 14845 degrees if freedom.
a)
b)
Figure 2.1 a) Concentration profiles of the C3S, H2O and CSH in the perfectly mixed
reactor model; b) Geometric profile of the single paste mutlti-physics model.
The diffusion coefficients of C3S, H2O, CSH and COH in the water zone are
assumed to be 1.0E-10, 1.0E-8, 1.0E-10, 1.0E-10 m2/s respectively, and their diffusion
coefficients in the cement zone are assumed to be 1.0E-10, 1.0E-5, 1.0E-10, 1.0E-10
m2/s respectively. The diffusion coefficient of water in the cement zone was described as
a function of the reaction rate. The hypothesis relies on the fact that the chemical reaction
rate will decrease as the reaction going along, and the produced CSH and COH will
adhere on the surface of the cement which would gradually prevent the water invading
into the unhydrated cement. The outer water boundary is of insulation type in both mass
and heat transportations. The thermal conductivity of water is 0.7 W/(m·K)and the value
is 3 W/(m·K)for cement. The densities of water and cement are 1000 kg/m3 and 2000
kg/m3. And the heat capacity of water and cement are 4200J/(kg·K) and 2000J/(kg·K).
21
2.3.3 Results and Analyses
2.3.3.1 Hydration Process Analysis and Simulation Validation
At the very start of the hydration process, the cement paste was surrounded by
water and located at the center of the water zone. This simulation was proposed based on
the assumption that adequate water was supplied in the chemical reaction, and this
assumption is valid since most water serves as a lubricant in cement mix. Figures 2.2
illustrated the various aspects of cement hydration. The concentration of C3S decreases
as the reaction continues. The outer rim was consumed in the chemical reaction together
with the incoming water. Heat was generated in the reaction, and the red arrows illustrate
the direction and the magnitude of the heat flux. The longer the arrows, the larger of heat
flux values.
22
a)
b)
c)
d)
Figure 2.2 a) The concentration of C3S (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot
Figure 2.3 showed the migration process of water into the cement paste. Heat flux
occurred at the water-cement interface where hydration process happened at the highest
rate. The resultant CSH was first produced at the water-cement interface as shown in
Figure 4 and the red color in Figure 2.4 shows the development of area with CSH.
23
a)
b)
c)
d)
Figure 2.3 a) The concentration of H2O (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot
24
a)
b)
c)
d)
Figure 2.4 a) The concentration of CSH (surface plot), and heat flux (arrows) after 1 sec
and b) 100sec, c)1000 seconds, and d) 3D surface plot
At the initial period, the highest temperature appeared at the water-cement
interface where the chemical reaction initiated as shown in Figure 2.5. As the reaction
was ongoing, the location of the highest temperature distribution transferred to the center
of the cement zone. Figure 2.6 demonstrated the temperature profiles of three different
locations. Position 0.0 lies in the center of the cement zone, Position 0.005 stands right on
25
the cement-water interface, and the Position 0.01 located on the outer water boundary as
shown in Figure 6b, and the number 0.0, 0.005 and 0.01 stands for its values on the x
axis. The explanation for this phenomenon is that the cement paste has a much smaller
heat capacity compared with water, while its thermal conductivity is much higher (around
four times) than the value of the water. And this could further validate the reliability of
the multi-physics simulation.
a)
b)
c)
d)
Figure 2.5 a) The temperature distribution (surface plot), and heat flux (arrows) after 1
sec and b) 100sec, c)1000 seconds, and d) 3D surface plot
26
300.20
Position0.0
Position0.005
Position0.01
Temperature(K)
300.16
300.12
300.08
300.04
300.00
0
20
40
60
Time(s)
a)
27
80
100
b)
Figure 2.6 a) Temperature variations at different positions of hydration system, b) the
locations of the temperature profile demonstrated in Figure 2.6a.
Figure 2.7a described the temperature discrepancy between the cement and the
water during the chemical raction, and Figure 2.7b illustrated the heat flux at the
hydration boundary. Quite similar trends appeared in the two Figures. Local heat flux has
a proportional relationship with the temperature gradient as described in Eq. 2.8. And
Figure 2.7 exhibited the analogous tendency between the temperature discrepancy and the
heat flux at the hydration boundary which supported the validation of this simulation
quite well.
28


Qq =−k ∇T
(8)


where Qq is the local heat flux, [W·m−2], Qq is the local heat flux, [W·m−2], k is the

material's conductivity, [W·m−1·K−1], ∇T is the temperature gradient, [K·m−1] .
40
0.10
Heal_flux(mol/m2*s)
Temperature Diffenence(K)
0.12
0.08
0.06
0.04
0.02
0.00
30
20
10
0
0
20
40
60
Tims(s)
80
100
0
20
40
60
Time(s)
80
100
b)
a)
Figure 2.7 a) The temperature discrepancy between cement and water, and b) heat flux at
the hydration boundary.
0.00018
Water_flux(mol/m2/s)
0.00016
0.00014
0.00012
0.00010
0.00008
0.00006
0.00004
0.00002
0
20
40
60
Time(s)
80
100
Figure 2.8 Water flux at the hydration boundary
29
As the ongoing reaction, more and more CSH was generated at the boundary
between the cement and the water, and this CSH would prevent water flowing into the
internal part of the cement, so the water flux decreases as the chemical reaction continues
as illustrated in Figure 2.8. Figure 2.9 and 2.10 plot the evolution of the concentration of
CSH and C3S in the hydration system. The changes of these concentrations are direct
indicators of chemical reaction rates. The plots in logarithmic scale clearly indicate that
the hydration rate is decided by an exponential process (possibly is controlled by rate of
0.00012
0.00012
0.00010
0.00010
c_CSH(mol/m)
c_CSH(mol/m)
diffusion of water into cement paste).
0.00008
0.00006
0.00004
0.00002
0.00008
0.00006
0.00004
0.00002
0.00000
0.00000
200
400
600
Time(s)
800
1000
1
10
100
1000
Time(s)
b)
a)
Figure 2.9 a) Concentration of CSH in the whole hydration system, and b) the logarithmic
time axis plot
30
0.00024
0.00022
0.00022
0.00020
0.00020
c_C3S(mol/m)
c_C3S(mol/m)
0.00024
0.00018
0.00016
0.00014
0.00012
0.00018
0.00016
0.00014
0.00012
0.00010
0.00010
0
200
400
600
Time(s)
800
1
1000
10
100
1000
Time(s)
b)
a)
Figure 2.10 a) Concentration of C3S in the whole hydration system, and b) the
logarithmic time axis plot
2.3.3.2 Relating the Chemical Reactions to the Strength of Cement Paste:
A
Microstructure Based Approach
The coupled multiphysics simulation gave the evolution of hydration reactions
across the cement paste. The evolution of cement hydration and its relationship to the
bonding strength of a cement paste can be estimated by considering the facts that a)
hydration products such as CSH is responsible for the strength development; and b) the
degree of hydration is non-uniformly distributed over the cement particle.
Figure 2.11 showed the concentration of CSH along the radius of the cement
particle.
If assume that the strength of cement paste is proportional to the CSH
concentration, the center of the area enclosed by CSH curves can be calculated (which is
called hydration radius in this paper). As hydration continues, the hydration radius
continues to increase (Figure 2.12). Similarly, the CSH concentration at the boundary of
31
the cement paste increased (Figure 2.13).
These two quantities help to define the
concentration of CSH gel.
2.5
c_CSH(mol/m3)
2.0
1.5
1.0
0.5
0.0
0.000
0.001
0.002 0.003
Radius(m)
0.004
0.005
b)
a)
Figure 2.11 a) Sketch map of hydration radius, and
3.5
3.5
3.0
3.0
2.5
2.5
Hydration radius(m)
Hydration radius(m)
b) The concentration of CSH along hydration radius after 1000 sec
2.0
1.5
1.0
0.5
2.0
1.5
1.0
0.5
0.0
0.0
-0.5
-0.5
0
200
400
600
Time(s)
800
1000
1
10
100
1000
Time(s)
b)
a)
Figure 2.12 a) Hydration radius increases with time, and b) the logarithmic time axis plot
32
0.04
0.03
0.03
c_CSH(mol/m2)
c_CSH(mol/m2)
0.04
0.02
0.01
0.00
0.02
0.01
0.00
0
200
400 600
Time(s)
800
1000
10
100
1000
Time(s)
b)
a)
Figure 2.13 a) Concentration of CSH at the hydration boundary increases with time, and
b) The logarithmic time axis plot
2.3.3.3 The Effects of the Size, Shape and Distribution on Hydration Kinetics
Particle size
The effects of the particle size on the cement hydration were investigated by
changing the size of hydrating particles from mm, um to nm range. An example of FEM
mesh and the simulated multiphysical field is shown in Fig. 2.14.
The predicted degree of hydration (percentage of cement particle consumed by
the chemical reactions) versus the time process is nomalized in the time axles by the
characteristic time. A chracteristic time is defined as the time required for achieving 50%
degree of hydration. The results of normalized hydration curve for cement particles with
different size range are summarized in Figs. 2.15-2.18. Figure 18 indicates that the
normalized hydration curve for cement of different particle sizes can be described with a
single trend with reasonable accuracy. This might be a logic outcome of the continuous
33
model and diffusion parameters.
a)
34
b)
Figure 2.14 a) FEM mesh for cement particle surrounded by water; b) CSH gel and heat
flux around cement particle
35
1
0.1
Hydration Degree
0.01
1E-3
1E-4
Equation
y = P1*x/(P2 + x)
Adj. R-Square
0.993
Value
1E-5
1E-6
Standard Error
Concatenate
P1
0.94945
0.01192
Concatenate
P2
0.91697
0.08547
1nm
10nm
Hyperbl Fit of Concatenate
1E-7
1E-7 1E-6 1E-5 1E-4 1E-3 0.01
0.1
1
10
100
Normalized Time
Figure 2.15 Hydration degree VS. time normalized by the half time of nano-cement
pastes
36
1
Hydration Degree
0.1
0.01
1E-3
1E-4
Equation
y = P1*x/(P2 + x)
Adj. R-Square
0.98395
Value
1E-5
1E-6
1E-6
1E-5
Standard Error
B
P1
1.08217
0.04215
B
P2
0.94128
0.17953
1E-4
1E-3
0.01
0.1
1
10
Normalized Time
Figure 2.16 Hydration degree VS. time normalized by the half time of um size cement
pastes
37
1
0.1
Hydration Degree
0.01
1nm
10nm
1um
10um
100um
1mm
1E-3
1E-4
1E-5
1E-6
1E-7
1E-7 1E-6 1E-5 1E-4 1E-3 0.01
0.1
1
10
100 1000
Normalized Time
Figure 2.17 Hydration degree VS. time normalized by the half time of cement pastes with
various particle sizes
38
1
0.1
Hydration Degree
0.01
y = P1*x/(P2 + x)
Equation
Adj. R-Squar
0.95226
1E-3
1E-4
1E-5
1E-6
Value
Standard Erro
Concatenate P1
0.9755
0.01953
Concatenate P2
0.6067
0.0747
1nm
10nm
1um
10um
100um
1mm
Hyperbl Fit of Concatenate
1E-7
1E-7 1E-6 1E-5 1E-4 1E-3 0.01
0.1
1
10
100 1000
Normalized Time
Figure 2.18 Hydration degree VS. time normalized by the half time of cement pastes with
various particle sizes
Particle Shape
The effects of the particle shapes are investigated by simulating the hydration of
rectangular cement particles with different length to width ratio. The length to width
ratios varied from 1,2,3,5, 10. The effects of different widths of cement particles were
also taken into consideration for comparison. Example of simulation model and FEM
mesh are shown in Fig. 2.20.
39
40
Figure 2.19 FEM model to study the effects of particle shape on hydrations
The half time (defined here as time corresponding to 50% degree of hydration) of
various shaped cement pastes were listed in Table 2.1.
Figure 2.21 compares the
concentration of C3S for cement particles of different width. The quantitative results for
half time are summarized in Figure 2.22. To study the effect of particle shape, the half
time are normalized by the half time of cement particle with large length-to-width ratio
(corresponding to 1 dimensional hydration) and presented in Figure 2.23.
All the
normalized curves follow a similar trend. This indicates that the half time of a cement
paste of rectangular shape can be represented by the following relationship:
41
L
T0.5 = T0.5 ( w = ∞) ⋅ f   = g (w) ⋅
B
L
f 
B
(9)
where T0.5 is the Half Time of a rectangular cement particle, T0.5(w=∞) is the half time of
cement particle of infinite length (which is a function of its width w only, i.e., g(w)),
f(L/B) is the adjustment factor for the length-to-width ratio.
The specific functional format for the cement paste simulated in this study can be
obtained from Figs. 2.24 and 2.25, i.e.,


L
L
T0.5 = g (w) ⋅ f   = 43.576 w1.6463  0.2994 ln  + 0.321
B
B


(10)
With half time available, the evolution of the degree of hydration can be readily
estimated.
Table 2.1 Half time of cement particles of different sizes and different length-to-width
ratio
Length /
Width
1
2
3
5
10
Half Time (s)
Half Time (s)
Half Time (s)
Particle width=0.5mm
5
8.5
9.5
12
14.5
Particle width=1mm
14
21
27
34
42
Particle width=2mm
41
68
89
123
144
42
(a)
(b)
43
(c)
Figure 2.20 a) C3S concentration after 100 seconds for the cement particle with a width
of 0.5mm and different length-to-height ratios. b) C3S concentration after 100 seconds
for the cement particle with a width of 1mm and different length-to-height ratios. c) C3S
concentration after 100 seconds for the cement particle with a width of 2 mm and
different length-to-height ratios
44
160
HalfTime0.5mm
HalfTime0.8mm
HalfTime1mm
HalfTime1.5mm
HalfTime2mm
140
Half Time (s)
120
100
80
60
40
20
0
0
2
4
6
8
10
Length / Width
Figure 2.21 The half time of cement particles of different width and shape
45
Normalized Half Time by that of infinite length
1.0
0.9
0.8
0.7
0.6
w=0.5mm
w=0.8mm
w=1.0mm
w=1.5mm
w=2.0mm
0.5
0.4
0.3
0.2
0.1
0.0
0
2
4
6
8
10
Length-to-width Ratio
Figure 2.22 the normalized half time (by that of particle with infinite length-to-width
ratio) for cement particles of different width and shapes
Figure 2.23 Relationship between half time and width of cement particle (of infinite
length)
46
Figure 2.24 Relationship between normalized half time and length-to-width ratio
Simulations were also conducted for cement of equal areas but different shapes
(circle versus rectangular). The results are shown in Figs. 2.35-2.27. With equal area,
cement particles of round shapes hydrate faster than that of rectangular shapes. This is
consistent with the fact that the length of diffusion for water and ions are smaller for
rounded shaped particles (such as circular). The corresponding hydration rate would be
higher since the rate of cement hydration is diffusion controlled.
shapes can be approximated by these fundamental shapes.
47
Cement of irrgular
Figure 2.25 C3S concentration of the cement pastes of both rectangular shape and circle
shape after 50sec. (left) and 100sec(right)
90
80
HalfTimeRect
HalfTimeCircle
70
Half Time(s)
60
50
40
30
20
10
0
0
2
4
6
8
10
Length / Width
Figure 2.26 the half time of various cement pastes of both rectangular shape and circle
shape
Estimation of Hydration Development from Cement Particle Size Information
48
The size distribution of cement particles can be described by the Rosin-Pammler
distribution function G(x),
𝐺(𝑥) = 1 − exp (−𝑏𝑟𝑟 𝑥 𝑛𝑟𝑟 )
(10)
where G(x) is the cumulative weight in [g] of the particles with diameter x. The constant
𝑏𝑟𝑟 and 𝑛𝑟𝑟 depend on the fitness of the cement. This equation is valid for 1 gram of
cement.
As example illustration on the dependent of constants on cement fitness:
•
•
•
Fine: specific area 550m2/kg, 𝑏𝑟𝑟 =0.067, 𝑛𝑟𝑟 =1.030
Fine: specific area 420m2/kg, 𝑏𝑟𝑟 =0.041, 𝑛𝑟𝑟 =1.076
Fine: specific area 300m2/kg, 𝑏𝑟𝑟 =0.023, 𝑛𝑟𝑟 =1.107
Utilizing the concept of half time, the degree of hydration for single cement
particle can be described as
uD (t)=1-A𝑒
𝐵
𝑡
𝑡𝑓
(11)
where A and B are constants, 𝑡𝑓 is the half time of some specific cement.
At the beginning of the hydration, t=0 and uD (t)=1-A=0, we get A=1 and when it come
to the half time, t=𝑡𝑓 , and uD (t)=1-𝑒 𝐵 =0.5, so B equals to -0.67.
49
uD (t ) =
1 − exp(−0.67
t
)
tf
(12)
For the whole cement system, the average hydration degree U is the weighted average of
degree of hydration of all the cement particles,
U=
∫ u G( x)dx
∫ G( x)dx
D
(13)
With the relationship between the particle size/shape and the half time together with the
particle size distribution, the bulk degree of hydration can be readily estimated.
For example, for circular shaped cement particles, the half time is related to the
particle radius in the following relationship (from the results of multi-physics simulation).
t f = -63+123738.09524 X-4.09286E7 X 2 +1.13333E10 X 3
50
(14)
1000
Y =-63+123738.09524 X-4.09286E7 X2+1.13333E10 X3
Half Time(s)
800
HalfTime
Polynomial Fit
600
400
200
0
0.001
0.002
0.003
0.004
0.005
Radius(m)
Figure 2.27 Relationship between half time and radius of cement particle
The bulk degree of hydration can be estimated by substituting Eq. (14) into Eq.
(13) and from numerical integration. An example is given in Eq. (15).
U=
∫
0.002
0.001
∫ u G( x)dx =
∫ G( x)dx
D
[1 − exp(−0.67
(15)
0.004
0.005
t
t
t
)][1 − exp (−0.067 x1.030 )]dx + ∫ [1 − exp(−0.67 )][1 − exp (−0.041x1.076 )]dx + ∫ [1 − exp(−0.67 )][1 − exp (−0.023 x1.107 )]dx
0.002
0.004
tf
tf
tf
∫
0.002
0.001
[1 − exp (−0.067 x1.030 )]dx + ∫
0.004
0.002
[1 − exp (−0.041x1.076 )]dx + ∫
0.005
0.004
[1 − exp (−0.023 x1.107 )]dx
This investigation indicates that the bulk cement hydration can be estimated from the
hydration of the single cement particle after accounting the effects of the particle size,
51
particle shape and mutual interference. Multiphysical simulations provides a way to
predict cement hydration kinetics from the basic hydration reactions.
2.3.3.4 Half Time Analysis for Cement Pastes With Various Particle Sizes
Half time refers to the time it takes for the reactants to drop to 50% of its original
value, and it serves an important index to evaluate the maturity of the concrete. The new
multi-physics model was applied here to analyze the half time of the cement pastes with
various particle sizes.
Figure 2.28 illustrated the mesh grids of the finite element model. Various grid
sizes were applied here for the calculation efficiency and relatively high accuracy.
Continuous boundary conditions were set up between the cement pastes in both thermal
analysis field and diffusion analysis field, and isolated boundary was used for the outer
boundary.
Figure 2.29 showed the half time of cement pastes with various particle sizes, and
Figure 2.35 showed the hydration process of the cement pastes. The expansion of the
CSH gels was realized by applying a diffusion factor for the CSH into the water.
52
1000
1000
800
800
600
600
Half Life(s)
Half Life(s)
Figure 2.28 Mesh grids of the Finite Element Model
400
400
200
200
0
0
0.001
0.002
0.003
0.004
Radius(m)
1E-3
0.005
a)
0.002
0.003 0.004 0.005
Radius(m)
b)
Figure 2.29 a) Half time of cement pastes with various particle sizes, and b) the
logarithmic time axis plot.
53
a)
b)
c)
d)
Figure 2.30 Temperature distribution(surface plot), concentration of CSH(contour), and
heat flux(arrows) after a) 1 second; b) 100 seconds; c) 500 seconds; d) 1000 seconds.
Discussion on the Diffusion Theory
Cement hydration is a diffusion controlled process.
The soundness of the
diffusion theory directly affects the validity of conclusion from multiphysical simulation.
Four modes of transport are commonly found in porous media (Mason and Malinauskas
1983). These include 1) Free-Molecule or Knudsen Flow (DK), in which the gas density
is so low that collisions between gas molecules can be ignored compared to collisions of
54
gas molecules with the porous medium walls. 2) Viscous or Convective or Bulk Flow (η
), in which the gas acts as a continuum fluid driven by a pressure gradient, and gas-gas
collisions dominate over gas-wall collisions. 3) Ordinary or Continuum Diffusion, in
which the different species of a mixture move relative to each other under the influence
of concentration gradients (concentration diffusion), or external forces (forced diffusion)
(together Dij), thermal gradients (thermal diffusion) (αij ). Here gas-gas collisions also
dominate over gas-wall collisions. 4) The fourth mode, Surface Flow or Diffusion (DiS),
in which molecules move along a solid surface in an adsorbed layer.
Figure 2.31 Percolation structure at the interface of unhydrated cement
A recent study by the use of nuclear resonant reaction analyses has unveiled the
percolation structure on the interface of unhydrated cement (Fig. 2.19, Livingston and
Schweitzer 2007; Balaguru and Chong 2007). The unhydrated cement is found to be
surrounded by a semi-permeable surface layer, gel layer and a calcium-leached zone, all
in the nanometer thickness. The surface zones evolve dynamically involving the break
down of the C-S-H gel layer, the leachate of calcium hydrate, and the migration of water
55
towards unreacted cement. As the size of cement reduces, the interaction of water and
ions with the channel wall can not be ignored. The traditional diffusion theory might need
to be revisited to describe the transport process across the nanoporous structure of
cement.
2.4 CONCLUSIONS
A multi-physics numerical model was developed to predict the development of
cement paste hydration. The chemical reaction theory, heat transfer theory and diffusion
theory were coupled in this model. The simulation results were validated based on field
test phenomenon and experiential equations, and promising results were achieved.
Besides predicting the development of the hydration process, this model also proposed a
microstructure based approach to relate the chemical reactions to the strength of cement
paste. Preliminary results showed that this numerical model can help predict the early
stage concrete behaviors. The existing diffusion theory predicts that the size effects of
cement particles on the chemical hydration can be accounted for by normalizing the
reaction time with characteristic time. With the unveiling of the nanoporous structure on
the surface of unhydrated cement, the diffusion theory, which was based on continuous
model assumption and ignores the particle-tunnel interactions, might need to be revisited.
Compared with traditional cement hydration simulation model, the multi-physics
model proposed here has several advantages.
1. Chemical reaction was bypassed in the traditional cement hydration simulation
model for the simplification purpose. While in this multi-physics model, the exact
chemical reaction process was realized with the accurate reaction formula. What’s
56
more, the contribution of the reactants concentrations to the reaction rate was
taken into consideration. And the heat generated during the hydration process was
explicitly expressed in the chemical reaction model other than a parameter in an
empirical PDE.
2. The true meaning of the concept of the hydration degree was applied here, the
consumed percentage of the reactants during the chemical reaction. So this model
put forward a more direct calculation of the hydration degree compared with the
empirical partial differential formula.
3. During the cement hydration process, it’s considered to be a formation of the
contacts between the expanding cement pastes. And this process of formation was
exactly reproduced in this proposed model by the diffusion of the products from
the surface of the cement paste. While former models usually use a empirical
expansion factor to simulate this expansion and contact formation.
4. The detailed distribution of the temperature field, concentration of both reactants
and products at any specific time of the hydration process could be described and
predicted in this model. So it’s a true meaning of the real multi-physics simulation
of the cement hydration process which illustrates a direct description of the whole
kinetic hydration development.
5. Various shapes of cement pastes could be described and simulated in this model,
so it’s not necessary to simplify the cements to the round shapes anymore.
6. The chemical reactions of the cement particles influenced the hydration process of
each other. The influences are partly induced by the overlaps of the concentration
of the reactants and the products. And the reaction heat of each particle could also
57
influence the hydration process of its neighbor particles. This proposed model
could take these complicated mutual influences into consideration. And this
feature qualified the model for good quality simulation under almost any
conditions.
However, this preliminary multi-physics model also comes with several
disadvantages and limitations.
1. Much longer calculation time is needed for this simulation to process the coupled
kinetic hydration development, so it’s not easy to conduct large scale simulation
using a PC.
2. Finite element method was employed in this simulation, so algorithm
convergence problems might arise especially when dealing with the multi-scale
problems.
3. The simulation currently still falls within 2 dimensional range.
Preliminary results showed that this numerical model can help predict early stage
concrete behaviors. Upon further development, it can provide supporting information for
the construction of concrete pavement from the fundamental chemical constituents and
the microstructures.
58
CHAPTER 3 DEVELOPMENT OF INNOVATIVE TECHNOLOGY
FOR TRANSPORT PROPERTIES OF EARLY STAGE CONCRETE
3.1 OVERVIEW OF RESEARCH THRUST
The research looked into developing innovative instruments to determine the
transport properties of concrete that can serve as input parameters for multiphysical
simulations.
Two important aspects were investigated. One is the thermal properties
measurement by the use of thermal Time Domain Reflectometry technology.
This
technology provides a non-destructive method to measure the thermal properties (such as
the thermal conductivity and heat capacity) which are crucial for the compatibility of
concrete.
3.2 THERMAL PROPERTIES MEASUREMENT BY DEVELOPING
THERMAL TDR TECHNOLOGY
Thermal properties are among the most important factors determining the
durability of concrete. The capability of the thermal measurement was integrated into
Time Domain Reflectometry. This combines an electromagnetic wave pulse and thermal
pulse technology to estimate the thermal properties. Initial validation was conducted on
soils. The results were encouraging. Further application of this technology for concrete
is currently underway.
59
3.2. 1 Theoretical Basis of Thermal Pulse Technology (TPT)
Thermal pulse technology (TPT) measures the thermal properties of a material by
generating a heat pulse and measuring its propagation and attenuation. Typically, a line
heat pulse of short duration is generated. The thermal pulse propagates in the cylinder
directions away from the line heat source (Figure 3.2a). This causes a radial propagating
temperature disturbance which is a function of time and distance from the heat source
(Figure 3.2b).
Heat Pulse
Heater
Tested
Materials
Heat
Flux
Fl
Figure 3.1 Schematic of thermal pulse technology (one dimensional heat transfer with no
thermal exchange in the vertical direction)
60
Temperature (oC)
1.0
Source Pulse
Thermal Responses
0.8
0.6
0.4
tm
0.2
0.0
0
t0
∆Τm
10
20
30
Time (s)
40
50
Figure 3.2 illustration of the source pulse and temperature responses
Data analysis for the thermal pulse technology is based on modeling the thermal
diffusion process in continues homogeneous materials. The fundamental solution for the
thermal field distribution around an infinite line heat source has been solved for the axialsymmetric system (de Vries 1952, Kluitenberg et al. 1993, 1995, Bristow et al. 1994).
For a line heat pulse of duration t0, the temperature disturbance at distance, r, away from
the heat source is described by Equation (5).
=
∆T ( r , t )
Q
−r 2
−r 2
[ Ei(
) − Ei(
)]
4πα
4α (t − t0 )
4α t
(5)
where ∆T denotes the temperature variation (oC or F), t is time (s), t0 is the duration of
the heat pulse (s), r is the radial distance (m), Ei (x) is the exponential integral, α is the
61
thermal diffusivity, and Q denotes the strength of the heat resource, which is calculated
by,
Q = q / ρc
(6)
where q is the quality of heat release per unit length of the probe (W/m), ρ c denotes the
volumetric heat capacity (J/m3/K).
The total volumetric heat capacity of the soil system includes those of water, soil
solids and air. However, the contribution of the air to the total heat capacity is negligible
and is thus typically ignored for practical purpose. The total volumetric heat capacity can
then be calculated as:
=
ρ c ρb cs + ρ w cwθ
(7)
where ρb is the bulk density of the soil, cs is the specific heat of the soil particles
(kJ/kG/K), ρ w is the density of water (kG/m3), and cw is the specific heat of water
(kJ/kG/K) (Lackner and Amon 2005, Naidu and Singh 2004).
The thermal diffusivity α in Equation (5) can be calculated by Equation (8)
(Kluitenberg 1993, Bristow 1994, Heitman 2007),
=
α
r2
t
1
1
− ] / ln[ m ]
[
4 (tm − t0 ) tm
(tm − t0 )
62
(8)
where tm denotes the time when the maximum temperature change ∆Tm occurred (G. J.
Kluitenberg 1993; Keith L. Bristow 1994; Heitman J.L. 2007), the other quantities are
defined as before.
The heat capacity, ρ c , the thermal conductivity, λ , and the thermal diffusivity, α
, are related according to Equation (9). Thus only two of the three thermal properties are
independent (Kluitenberg 1993).
λ = αρ c
(9)
3. 2.2 SENSOR DESIGN AND PERFORMANCE ASSESSMENT
The Thermo-TDR sensor was designed by adding thermal pulse generation and
measurement functions to a conventional TDR parallel probe. The probe geometry
referred to that used by Ren et al. 1999 and Heitman et al. 2007. The rods are 40 mm in
length and spaced 6 mm apart. The diameter of the probe rod is around 1 mm. The rods
are short in length and relatively low accuracy is required in civil engineering application
compared with soil science application, so it’s not necessary to conduct the spacing
calibration using the immobilized agar. The relative error usually falls within 5% without
this apacing calibration which is allowable in civil engineering application. This design
achieved an electrical impedance of around 150 when exposed to the air (O’Connor and
Dowding 1999). Instead of solid rods for traditional TDR probe, hollow steel rods were
used for the thermo-TDR probes. A resistance heater was embedded inside the central
rod to generate the heat pulse. Three type-K thermocouples were installed in each rod
respectively. The tubes were then backfilled with high thermal conductive epoxy. Figure
63
3.3a shows the schematic of the sensor probe design. Figure 3.3b shows the prototype of
the fabricated thermo-TDR sensor.
6 mm
(a)
Sensor probe
Thermocouple
wire
(b)
64
reading
Connect to TDR unit
Figure 3.3 a) Schematic design of the thermal-TDR probe; b) photos of the fabricated
thermo-TDR probe
3.2.2.1 Experimental Evaluation of the TDR Function
The performance of the TDR function by the thermo-TDR probe was first
evaluated by making measurements in the ASTM standard fine sand and a glacial till.
The glacial till was classified as clay with low plasticity (CL) by the USCS classification
system.
In the experiments, the thermo-TDR probes were installed in soil samples
prepared with different water contents and densities. TDR signals were acquired for each
sample.
The measured signals are plotted in Figure 3.4. For both sand and clay samples,
the TDR signals show systematic trends of change with increasing water content. This
indicates the TDR function is sensitive to the change of soil physical properties.
65
1.0
Relative Voltage(V)
0.8
Dry Sand
w=4%
w=8%
w=12%
0.6
0.4
0.2
0.0
-0.2
5.4
5.6
5.8
Scaled Distance(m)
6.0
1.0
Dry Clay
w=5%
w=10%
w=15%
Relative Voltage(V)
0.8
0.6
0.4
0.2
0.0
-0.2
5.4
5.5
5.6 5.7 5.8 5.9
Scaled Distance(m)
6.0
Figure 3.4 Influence of water contents on TDR signals measured by the thermo-TDR
probe: a) sand and b) clay
3.2.2.2 Experimental Evaluation of the TPT Function
The evaluation of the thermo-TDR sensor design involved assessment of the
thermocouples and the heat pulse generation function.
66
Temperature by Built-in Thermocouples (oC)
(a)
90
Thermocouple 1 (center rod)
Thermocouple 2 (side rod 1)
Thermocouple 3 (side rod 2)
80
70
60
50
40
30
20
20
30
40
50
60
70
Actual Temperature (oC)
80
90
(b)
Figure 3.5 a) Experiment set up for calibrating the built-in thermocouples; b) Results of
calibration
Evaluation of the Built-in Thermocouples
67
A mercury thermometer was employed as a reference base to evaluate the
performance of the thermocouples installed inside the thermo-TDR rods.
For this
purpose, both the thermometer and thermo-TDR probe were simultaneously placed in
container with water of different temperatures. The thermocouples were read by an eight
channel USB-based data sampling unit TC-08@ by Pico Technology Inc.. The mercury
thermometer was read manually.
The good linear relationship indicated the
thermocouples were properly installed.
Evaluation of Thermal Pulse Function
The thermal pulse function of the Thermo-TDR sensor was also evaluated. The
experiment set up is shown in Figure 3.6. The Thermo-TDR sensor was installed
vertically into the center of the specimen. Various power sources were used to power the
heater installed in the center rod, and test results showed that the sensor could work
efficiently even with very limited power supply. The duration of heat pulse was
controlled by a switch. The generated heat pulse and propagation were both measured by
the built-in thermocouples installed inside the rods.
Figure 3.7 shows the typical
temperature curves. The thermal responses under various heat pulse durations were also
evaluated.
68
Figure 3.6 Photo showing the installation of thermo-TDR probe in clay
Heat Pulse Temperature(oC)
26.4
45
Heat Pulse
40
26.2
35
Thermal Response
26.0
30
25.8
25
20
Thermal Response Temperature(oC)
50
0
30
60
90
Time(s)
120
25.6
150
Figure 3.7 An example of measured thermal pulse response in clay
69
W%=8%,Th=10sec
Tempreture(oC)
24.0
Sensor A
Sensor B
Sensor C
23.6
23.2
22.8
20
40
Time (s)
60
(a)
W%=8%,Th=30sec
24.4
Sensor A
Sensor B
Sensor C
Tempreture(oC)
24.0
23.6
23.2
22.8
20
40
60
Time(ms)
80
100
(b)
W%=8%,Th=1min
Sensor A
Sensor B
Sensor C
Tempreture(oC)
24.0
23.6
23.2
22.8
40
80
120
Time(ms)
160
(c)
Figure 3.8 Effects of thermal pulse duration on the thermal responses measured in sand of
8% water contents: a) 10 seconds, b) 30 seconds, c) 1 minute (sensor A: thermocouple in
center rod, sensor B and C: thermocouple in side rods)
The duration of the thermal pulse was accurately controlled once the testing
parameters were set. The measured heat responses in soil with different water contents
are shown in Figure 3.9. As shown in these Figures, the variation of soil water content
70
and density caused a systematic trend of change in the thermal pulse responses.
Typically, the higher the soil water content, the lower the maximum amplitude of the
propagating heat pulse.
These evaluations indicated the TPT function achieved the desired capability and
sensitivity.
Dry Sand
w=4%
w=8%
w=12%
Thermal Response (K)
0.25
0.20
0.15
0.10
0.05
0.00
0
20
40 60 80 100 120
Time (s)
(a)
71
Dr y Clay
w=5%
w=10%
w=15%
THermal Response (K)
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
20
40 60 80 100 120
Time (s)
(b)
Figure 3.9 The influence of soil physical properties on the measured thermal pulse
responses for a) sand; b) clay
3.2.3. Method of Thermo-TDR Signal Analyses
3.2.3.1 Analyses of the TDR signals
There are two commonly used approaches to determine the apparent dielectric
constant, Ka, from a TDR signal (Timlin and Pachepsky 1996), namely, 1) empirical
tangent line method; and 2) inversion analysis of the TDR signals.
The tangent line method generally follows the approach by Topp et al. (1980,
1982) and Baker and Allmaras (1990). This method locates the reflection points using
the tangent lines, i.e., slopes, from characteristic sections (“peak”, “valley” or maximum
slope) on the TDR signal. Both of these approaches require drawing tangent lines from
characteristic sections of the TDR signal to locate the reflection points.
72
The only
difference between them is the criterion of selecting these characteristic points (Figure
3.10). The procedures can be implemented by developing the computer algorithms. This
typically involves a smoothing process of the TDR signal; using numerical differentiation
to calculate the derivatives and find the locations of the characteristic points such as local
maximum or minimum, points with maximum slopes.
The intersections are then
determined from the tangential lines passing through the characteristic points (Yu 2003).
Figure 3.10 Two different tangent line methods to determine the second reflection from
TDR signal (Yu 2003)
The second approach to analyze a TDR signal is based on the inversion theory. In
this method, the measured TDR signal is employed to match the signal predicted using
the given material properties. The analysis can be conducted in either the time domain
(Yanuka et al. 1988; Timlin and Pachepsky 1996) or the frequency domain (Feng, et al.
1999; Lin 1999; Yu and Yu 2006). The advantage of this approach is that it physically
73
described the phenomena taking place in the TDR system. The shortcomings are the
longer computational time and the issue of non-uniqueness (Yu and Yu 2006).
For most practical applications, the empirical analyses of TDR signal were found
providing reasonable accuracy. This procedure was used in this study to analyze TDR
signals to determine the dielectric properties.
3.2.3.2 Method for Analyses of thermal pulse signals
Travel time analyses of thermo-TDR pulse propagation
Travel time analyses of thermo-TDR signal analyses involves the determination
the maximum amplitude of thermal pulse response Tmax and the corresponding travel time
t0. Both of them can be easily obtained from the measured thermal pulse response curves.
The thermal diffusivity, heat capacity and thermal conductivity can then be calculated
using Equations (5)-(10).
Model-based inversion analysis
The travel time analyses only utilize certain points in the measured signals. An
alternative procedure is to use inversion analyses to match the entire temperature curves.
The inversion analyses are based on the thermal pulse propagation model described by
Equations (5)-(9). Mathematically, the inversion analyses can be described as,
arg min(α , ρc) = ∆T (r , t ,α , ρc ) predict − ∆T (r , t ,α , ρc )measure
74
(10)
where function argmin () is to determine the parameters that minimize the value of the
target function. the ∆T (r , t ,α , ρc ) predict is the predicted temperature at a distance r from
the heat source, which is described by Equations (5)-(9), ∆T (r , t ,α , ρc )measure is the
actually measured temperature, the function
is the norm of a vector, which is typically
used as the root-mean-square.
In this study, a Matlab code was developed to implement the inversion procedure.
A forward model was first developed to predict the temperature process caused by a rapid
heat pulse. This involved the convolution of the responses from the infinite heat pulse
source and the temperature measured at the center rod, i.e.,
∆T (r , t ,α , ρ c ) predict = Eq.(5) ⊗ T (0, t )
(11)
where ⊗ is the convolution, T (0, t ) is the temperature process measured at the center
rod.
A sensitivity analysis for the model parameters was first carried out using the
forward model, such as the thermal conductivity of soil and the spacing of the thermoTDR rods. Figure 3.11 shows the results of the sensitivity analysis. In this Figure, the
thick blue curve was the source heat pulse. The other curves were the response heat
pulses measured at a certain distance away from the heat source. Figure 3.11a shows that
higher thermal diffusivity results in sharper response heat pulse. Similar observations
were found on the effects of spacing between the rods(Figure 3.11b), the thermal
response signals attenuated dramatically with the increased spacing between the rods.
75
Source heat pulse
1
Responses heat pulses
Alpha=1e-7
Alpha=3e-7
Alpha=5e-7
Alpha=7e-7
Alpha=9e-7
0.8
T
0.6
0.4
0.2
0
0
20
40
60
Time (s)
80
100
(a)
Source heat pulse
1
Responses heat pulses
r=1mm
r=3mm
r=5mm
r=7mm
20
80
0.8
T
0.6
0.4
0.2
0
0
40
60
Time (s)
100
(b)
Figure 3.11 Sensitivity analysis of thermal responses to a) the thermal diffusivities
(alpha); b) rod to rod distance
Inversion analyses were implemented using Simplex method, which is a standard
algorithm for the large scale linear optimizations. To ensure the results are stable, initial
values of parameters were set using the results from the travel time analyses.
76
3.2.4 Improvement of Thermo-TDR Probe Design and Inversion Analyses by the
FEM Analyses
It was observed that the model-predicted temperature response curves did not
match the actually measured data in certain cases when implementing the inversion
analyses. An example is shown in Figure 3.12.
This Figure plotted the results of
inversion analyses on the thermal signal obtained from a dry clay sample.
The
temperature response curve predicted by the theoretical model did not match the
experimental data, even after parametric optimization. This discrepancy occurred due to
the deviation of the model assumptions to the actual thermal pulse attenuation process.
Temperature Response (K)
0.6
Measured thermal response
Thermal response by initial parameters
Thermal response after inversion analyses
0.5
0.4
0.3
0.2
0.1
0.0
0
20
40
60
Time (s)
80
100
Figure 3.12 Results of inversion analyses of thermal response signal in dry clay
77
As is known, the theory of the thermal pulse technology (i.e., Equations (5)-(9)) is
based on two assumptions: 1) the heat source is an infinite line source (and thus the
thermal pulse disturbance propagates in one-dimensional cylindrical mode), and 2) the
soil surface is thermally insulated (de Vries 1952, Kluitenberg et al. 1993, 1995, Bristow
et al. 1994). The geometry of the commonly used thermo-TDR sensor could not exactly
satisfy these assumptions, i.e., 1) the sensor probe only has limited length and 2) there is
heat exchange with the air at the surface of the soil layer. To study the effects of these
factors, numerical simulations by the Finite Element Method (FEM) were conducted.
The finite element model was developed with an axial symmetry using the general
FEM package COMSOL@. An example is shown in Figure 3.13. For all the models, a
reference point was selected at the location that was 5 mm away from the middle of the
center rod. (The 5 mm distance is the center-to-center distance between the fabricated
thermo-TDR rods. The depth was set to be half the length of the center rod as this was
where the thermal couples were installed in the actual probe design.) The background
temperature was set to be 272K. An instantaneous heat pulse of 300 K (or ΔT=8°C) was
applied along the length of the center rod. The temperature responses at the reference
point were obtained after post-processing FEM simulation results. The influence of probe
lengths and thermal boundary conditions at the reference point were studied as shown in
the following context.
78
Reference point 5 mm away
from the middle of center rod
Surface boundary (air/soil)
Figure3. 13 Schematic of Finite Element Model
3.2.4.1 Effects of finite probe lengths on the temperature process
FEM models of thermo-TDR probe with different lengths (i.e, 40 mm, 100 mm
and 200 mm) were created. The spacing between the rods was assumed to be 5mm, so
the cprresponding length to spacing ratioes are 8,20 and 40.
A step heat pulse with a
magnitude of ΔT=8°C was applied along the length of the center rod. Figure 3.14 shows
the temperature field distribution 100 seconds after the thermal pulse was applied. The
direction of heat flow was also shown in this Figure. As seen in this Figure, the direction
of heat flow is not horizontal, which means the heat flow around a finite thermo-TDR
probe is not one dimensional. For a given probe length, the deeper the depth, the more
79
significantly the heat flow travels in the downward direction. This observation indicated
that the assumption of one-dimensional heat flow in the theoretical analytical model was
not accurate enough for a probe with a finite length. However, as seen in Figure 14, the
longer the thermo-TDR probe, the closer the heat flow in the horizontal direction at the
reference point. For example, when the probe length was 200 mm, the temperature field
was almost one dimensional along most part of the probe.
(a)
80
(b)
(c)
Figure 3.14 Influence of probe length on the distribution of temperature field and
direction of heat flow for various probe lengths a) 40 mm; b) 80 mm; and c) 200 mm
(unit scale of geometry: m)
Figure 3.15 compared the temperature response curves at the reference points for
the probes with different lengths. For a probe length of 40mm, the temperature response
81
at the reference point was lower, possibly due to the effects of two dimensional heat flow.
The curves however converged to a single one as the probe length continued to increase.
This indicated that for 40mm probe design, the one dimensional heat flow assumption
was not quite valid, i.e., the influence of heat propagation in the downward direction
could not be ignored.
Evidently, the thermo-TDR probe design used in previous
research (i.e., Ren et al. 1999 and Heitman et al. 2007) was not optimized to produce one
dimensional heat flow. One direct way to produce approximate one-dimensional heat
flow is to further increase the length of the thermo-TDR probes.
Thermal Response (K)
277.5
277.0
276.5
276.0
4CM Probe
8CM Probe
20CM Probe
275.5
275.0
0
20
40 60
Time (s)
80
100
Figure 3.15 Temperature responses for various probe lengths (40 mm, 80 mm and 200
mm)
3.2.4.2 Influence of the thermal boundary conditions
The theoretical model Equations (5)-(9) assumes the ground surface is thermally
insulated. In reality, the heat exchange with the air occurs at a rate depending on the
wind speed etc.. The influence of different types of thermal boundary conditions on the
82
temperature responses was studied.
FEM models were constructed assuming a probe
length of 40mm. Four different thermal boundary conditions were applied respectively,
i.e., thermal insulation at the ground surface, thermal boundary with low heat flux
exchange, thermal boundary with high heat flux exchange, and constant surface
temperature. The same background temperature and heat pulse was applied as described
in the earlier context, i.e., the background temperature was set to be 272K.
An
instantaneous heat pulse of 300 K (or ΔT=28°C) was applied along the length of the
center rod.
Among the four boundary conditions, the heat flux boundary described the actual
heat exchange process. The heat exchange with the air was governed by Newton’s law of
cooling, i.e.:

n ⋅ (λ∇=
T ) h c (Temb − T )
(12)
Where λ is the thermal conductivity, Temb is the ambient temperature, T is the surface
temperature of soil specimen, hc is the convective heat transfer coefficient which ranges
between 11 W/m2·°C (for static air) and 50 W/m2·°C (for wind speed of 3 m/s)
depending on the wind speed.
Figure 3.16 shows the distribution of the temperature field and the direction of
heat flux under different thermal boundary conditions. This Figure indicated the thermal
boundary conditions had appreciable effects on the temperature field distribution and the
direction of heat flow.
Figure 3.17 showed the temperature response curves at the
reference point. It indicated that the temperature response under the insulated boundary
83
(which as assumed by the theoretical model Equations (5)-(9)) was close to that under
low heat flux exchange boundary. The temperature response at the high heat flux
exchange showed slightly larger discrepancy. Thus corrections for the rate of heat flux
might be necessary for high rate of heat exchange at the ground surface (such as testing
under the high wind speed conditions).
(a)
84
(b)
(c)
85
(d)
Response Temperature (K)
Figure 3.16 The influence of thermal boundary conditions on the temperature field
distribution and heat flux a) Thermal insulation; b) hc=11W/(m2*K); c) hc=50W/(m2*K);
d) constant surface temperature (275K)
277.0
276.5
276.0
Insulation Boundary
Flux Boundary, H=11W/(m2.oC)
Flux Boundary, H=50W/(m2.oC)
Costant Temperature
275.5
275.0
0
20
40 60
Time (s)
80
100
Figure 3.17 The influence of thermal boundary conditions on the thermal responses
(thermal insulation is close to low flux surface boundary)
3.2.4.3 Corrections for un-optimized probe design
86
The FEM analyses indicated that there were deviations between the actual testing
method and the theoretical model assumptions, i.e., 1) the probe geometry does not
generate one-dimensional heat flow unless it is sufficiently long; 2) the heat exchange
with the air has an impact on the measured thermal responses depending on the rate of
convective heat exchange.
From the FEM analyses, it was found that the predicted temperature for the 200 m
long thermo-TDR probe under thermal insulated conditions could reasonably well
represent the temperature response for an infinite probe.
It is thus used as a reference
base for correction of experimental data. The coefficient of correction was calculated as:
Corr (t ) =
T (t , hc , l = 40mm)
T (t , insulated , l = 200mm)
(13)
where Corr(t) is the correction coefficient, which is a variable of time and distance,
T(t,insulated, l=200mm) is the FEM predicted temperature response curve at the
reference point for probe length of 200mm with thermal insulated boundary, T(t,hc,
l=40mm) is the FEM predicted temperature response curve at the reference point for
probe length of 40mm with heat exchange boundary.
Examples of calculated coefficients of correction are shown in Figure 3.18a.
With this, the measured temperature response (under the influence of two dimensional
heat flow and heat exchange thermal boundary condition) can be converted to a reference
condition of one dimensional heat flow and thermal insulation boundary conditions
(which matches with the theoretical model assumptions).
Equation (14).
87
The process is shown in
=
∆Tcorr ( t )
∆Tmeasure ( t ) + 273.15
− 273.15
Corr (r , t )
( C)
0
(14)
40 mm, thermal insulation
40 mm, heat flux boundary hc=11 W/(m2.K)
40 mm, heat flux boundary hc=50 W/(m2.K)
1.0002
Correction Factor
1.0000
20CM Insulation Boundary
0.9998
0.9996
0.9994
0.9992
0.9990
0
20
40
60
Time (s)
(a)
88
80
100
Corrected temperature responses:
1) 1D heat flow
2) Insulated thermal boundary
0.6
Response Data (oC)
0.5
Measured temperature responses:
1) 2D heat flow
2) Heat exchange boundary
0.4
0.3
0.2
0.1
Original Response Data
Revised Response Data
0.0
0
20
40
60
Time (s)
80
100
(b)
Figure 3.18 a) correction factor for 40mm probe under various testing conditions; b)
example of corrected thermal response process
Constant 273.15 was used in Equation (14) to convert the temperature between
Celsius to Kelvin. This is because the correction coefficients in Equation (13) and Figure
3.17, Corr(t), were calculated by Kelvin. The optimization criteria for inversion analyses
was revised from Equation (10) to Equation (15),
arg min(α , ρ c) = ∆T ( r , t , α , ρ c ) predict − ∆T ( r , t , α , ρ c )corr
(15)
The measured temperature response curves in dry clay before and after correction
is shown in Figure 3.18b. The temperature data is slightly higher after correction. This is
consistent with the corresponding pattern of heat flow and thermal boundary conditions
as explained in Figure 3.18b. The result of inversion analyses on dry clay is shown in
89
Figure 3.19, which achieves a better match compared with the uncorrected experimental
data (Figure 3.12). This validates the improvements in the signal analyses by accounting
for the discrepancy between the theoretical model and the laboratory experiment.
Corrected thermal response
Predicted thermal response by initial parameters
Predicted thermal response after inversion analyses
Temperature Response (oC)
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
20
40
60
Time (s)
80
100
Figure 3.19 Example of inversion analyses results on corrected data
3.2.5. RESULTS AND ANALYSES
3.2.5.1 Dielectric Properties and Calibration
The dielectric constants of soil samples at different water contents were
determined using the tangent line method. The results are shown in Tables 1 and 2.
Topp et al (1980) showed that for soils with a wide range of mineral content, a
single equation was adequate and was practically independent of soil bulk density,
ambient temperature, and salt content. This equation (Equation (16)) is now widely used
as a calibration curve and is referred as Topp’s equation,
90
θ = 4.3 ×10−6 K a3 − 5.5 ×10−4 K a2 + 2.92 ×10−2 K a − 5.3 ×10−2
(16)
where θ is the volumetric water content (i.e., volume of water compared to total volume
of soil).
This calibration equation has been confirmed by numerous authors on various soils and
currently is the most widely used calibration equation for the TDR applications.
Siddiqui and Drnevich (1995) developed an equation (Equation (17)) that related
TDR measured dielectric constant to gravimetric water content, w (i.e., mass of water
compared to mass of dry soil solids). This equation accounts for the effects of soil type
and density by incorporating two calibration constants. Besides, it uses the concept of
gravimetric water content which is commonly used by geotechnical community
(Drnevich et al. 2001a, 2001b).
ρw
Ka = a + b ⋅ w
ρd
(17)
where ρd is the dry density of soil, ρw is the density of water, a and b are soil-dependent
calibration constants, typically a is found to be close to 1, b is found to range from 7-11
(Yu and Drnevich 2004).
Equation (17) is used to establish the TDR calibrations between TDR measured
apparent dielectric constant and the water content. The results of calibration for sand and
clay are plotted in Figure 3.20. Both show good linear relationships. This validates that
the TDR function of the thermo-TDR probe works very well.
91
3
2.5
y = 10.72x + 0.9045
R² = 0.9951
Ka*ρ w /ρ d
2
1.5
1
Measured Data for Sand
0.5
Linear (Measured Data for Sand)
0
0
0.02
0.04
0.06
0.08
0.1
0.12
Water Content
(a)
3
y = 8.9538x + 1.0245
R² = 0.9952
2.5
Ka*ρ w /ρ d
2
1.5
1
Measured Data for Clay
0.5
Linear (Measured Data for Clay)
0
0
0.05
0.1
Water Content
(b)
92
0.15
0.2
Figure 3.20 Calibration of the dielectric constant by the thermo-TDR probe for a) sand,
and b) clay
3.2.5.2 Thermal Properties and Calibration
Both travel time analyses and inversion analyses were applied to analyze the
thermal pulse response signals.
For the physically measured signals, they tended to
be contaminated by noises. The effects require properly setting the operation parameters
of the thermo-TDR probe. For example, when the magnitude of applied heat pulse is
small, the received heat pulse contains significant noise (black curves in Figure 3.21).
Temperature (K)
This can make it difficult to apply the empirical travel time analyses.
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
-0.02
-20
Measured Output
Initial Output
Inverse Analysis
0
20
40
60
80
Time (s)
(a)
93
100 120 140
Temperature (K)
0.08
Measured Output
Initial Output
Inverse Analysis
0.06
0.04
0.02
0.00
0
10
20
30
Time (s)
(b)
94
40
50
Temperature (K)
0.4
0.3
0.2
Measured Output
Initial Output
Inverse Analysis
0.1
0.0
0
20
40 60
Time (s)
(c)
95
80
100
Measured Output
Initial Output
Inverse Analysis
Temperature (K)
0.3
0.2
0.1
0.0
0
20
40 60
Time (s)
80
100
(d)
Figure 3.21 Example results of inverse analysis on the thermal responses data from a) dry
sand; b) sand with 8% water content; c) clay of 10% water content, and d) clay of 15%
water content
The inversion procedure was applied to analyze these highly contaminated
signals. Example results are shown in Figures 3.21(a) and (b). The red curves were
obtained from the initial values (by guessing), and the blue curves stand for the results
after inversion analyses. The inversion procedures were found to be very robust even
under high noise conditions. From the experience conducted during the development of
the inversion analyses framework, the determination of the initial parameter is likely to
be subjected to an empirical judgment. The results by the direct travel time analyses can
serve as the initial parameters.
Tables 3.1 and 3.2 listed the estimated thermal conductivities and thermal
capacities of soils using the inversion procedure. The thermal conductivities by travel
96
time analyses were also shown for comparison. The results by the inversion analysis
procedure were believed to have better accuracy as it was less affected by signal noises.
TABLE 3.1 Results of thermal conductivity and volumetric heat capacity for sand
Water Content
0 (0)
4% (3.9%)
8% (7.4%)
12% (10.9%)
Dry
Density
(g/cm3)
1.60
1.33
1.41
1.50
Dielectric
Constant
Travel Time
Thermal
Conductivity
(W/(m·K))
0.390
0.267
0.327
0.421
2.20
2.87
5.93
9.65
Inversion Analyses
Thermal
Conductivity
(W/(m·K))
0.733
0.889
1.112
1.119
Volumetric Thermal
Capacity(J/(m3·K))
4.86e5
1.53e6
1.80e6
1.83e6
TABLE 3.2 Results of dielectric constant, thermal conductivity and volumetric heat
capacity for clay samples
Water
Content
0 (0)
5% (6.1%)
10% (11.9%)
15% (15.8%)
Dry
Density
(g/cm3)
1.28
1.35
1.33
1.31
Dielectric
Constant
1.60
4.69
8.03
10.03
Travel Time
Thermal
Conductivity
(W/(m·K))
0.226
0.329
0.405
0.478
Inversion Analyses
Thermal
Conductivity
(W/(m·K))
0.365
0.457
0.587
0.602
Volumetric Thermal
Capacity(J/(m3·K))
5.35e5
1.01e6
1.55e6
1.55e6
The relationship between the thermal conductivity of mixture and its constituent
components has been studied by a number of researchers. Among the various models, a
model based on weighted geometric mean is found to work well (McGaw 1969 ,
Woodside and Messner 1961).
Representation of soil mix in terms of the thermal
conductivity of individual phases ( λi ) and their respective volume fraction ( Φi ), the
formula is written as,
λ = λsΦ ⋅ λwΦ ⋅ λaΦ
s
w
97
a
(17)
where λ denotes the thermal conductivity of the soil, λs , λw and λa denote the thermal
conductivity of solid phase, water and air. Φ s , Φ w and Φ a denote the volumetric fraction
of the solids, water and air.
Taking the logarithm on both sides and substitute the representation for the
volumes of different phases, the thermal conductivity can be represented as,
 wρ d
wρ d
ρ 
ρ
log(λ ) = 1 −
− d  log(λa ) +
log(λw ) + d log(λs )
Gs ρ w
ρ w Gs ρ w 
ρw

(18)
By rearranging the terms, there is,
λ 
log 
 λa  = w log λw  + 1 log λs 
λ  G
λ 
ρd / ρw
s
 a
 a
(19)
When the results of thermal conductivities are plotted in the format described by
this equation, good linear relationships appear (Figure 3.22). This indicates the results of
thermal conductivities are reasonable.
98
0.1
y = 1.1682x - 0.0792
R² = 0.905
0
Log(λ)/(ρ d/ρ w)
-0.1
-0.2
-0.3
-0.4
By Inversion Analyses
-0.5
By Travel Time Analyses
-0.6
0
0.02
0.04
0.06
0.08
Water Content
0.1
0.12
(a)
0
Log(λ)/(ρ d/ρ w)
-0.1
y = 1.1326x - 0.3303
R² = 0.9483
-0.2
-0.3
y = 1.5747x - 0.4844
R² = 0.9579
-0.4
-0.5
By Inversion Analyses
-0.6
0
0.05
0.1
Water Content
(b)
99
0.15
0.2
Figure 3.22 Arrangement of data into the model format for sand and clay
The bulk heat capacity ρc is related to the heat capacity of its constituents by
Equation (20).
ρc =
ρd
ρ ⋅w
cs + d
c
Gs ρ w
ρw w
(20a)
or
ρc
1
=
cs + cw ⋅ w
ρ d / ρ w Gs
(20b)
When the results of heat capacities are plotted in the format described by this
equation, good linear relationships appear between ρc/ ρd versus w (Figure 3.23). This
validates the results of heat capacity.
1600
1400
ρc/(ρ d/ρ w)
1200
1000
800
y = 8049.8x + 541.55
R² = 0.6724
600
400
200
0
0
0.02
0.04
0.06
0.08
Water Content
100
0.1
0.12
(a)
1400
y = 5109.3x + 444.59
R² = 0.9529
1200
ρc/(ρ d/ρ w)
1000
800
600
400
200
0
0
0.05
0.1
Water Content
0.15
0.2
(b)
Figure 3.23 Results of thermal capacity normalized by the soil dry density versus water
content for Clay
3.3 THERMO-TDR PROBE RESPONSES FOR THE SOILS SUBJ ECTED TO
FREEZING-THAW PROCESS
In partially frozen soil, the soil thermal properties could not be measured
accurately because of the latent heat transfer process. Equation (21) was applied to
describe this case.
C
∂T
∂
∂T
∂T
− L=
(λ ) − J1C1
f Si
∂t
∂z ∂z
∂z
(21)
Where C is the volumetric thermal capacity of the soil (MJ/(m-3 ⋅ K)), T is
temperature (K), t is time (s), L f is the latent heat of fusion for water (J/kg), Si is the
mass rate of the ice formation (kG/ (m-3 ⋅ s)), z is the depth (m), λ is the soil thermal
101
conductivity (W/(m∙K)), J1 is the liquid water flux (m3/(m2·s)), and C1 is the volumetric
thermal capacity of water (MJ/(m-3 ⋅ K)).
Ca= C + L f ρ1
∂θ1
∂T
(22)
Ca is the apparent volumetric thermal capacity of the partially frozen soil system
(MJ/(m-3 ⋅ K)), it could be interpreted as the volumetric thermal capacity of the soil- water
system when the phase change between the liquid water and ice is happening. ρ1 is the
liquid water density (kg/m3), and θ1 is the volumetric water content of the soil (m3/m3).
λa= λ + ρ1 L f K
∂ψ 1
∂T
(23)
λa is the apparent thermal conductivity of the partially frozen soil system
(W/(m∙K)), K is the soil hydraulic conductivity, and ψ 1 is the matric potential.
Therefore, the heat transfer process in partially frozen soil can be described using
Equation (24), and this partial differential equation includes both the heat transfer by
conduction and latent heat transfer due to phase change.
Ca
∂T ∂
∂T
= (λa
)
∂t ∂z
∂z
(24)
A laboratory experiment was conducted to generate an assessment on the ability
of the thermo-TDR probe to study behaviors of partially frozen soils. A clay specimen
with a water content of 15% was prepared with the thermo-TDR probe installed. The
specimen was then placed in a freeze thaw cycle in a temperature controlled room. TDR
signals were automatically recorded by the computer at a time interval of 1 minute. The
temperatures of all three thermal couples were recorded, and there was also one
102
temperature sensor recording the environmental temperature. Thermal pulses were
generated during the course of the experiment, and both the heat pulse and the responses
were measured in a timely manner.
TDR signals were recorded during this freezing-thawing process. The liquid water
content decreased during the soil freezing period, and the electric impedance changed
accordingly during this ice-water phase change. This change was described using the
TDR signals in Figure 3.24. The freezing degree was defined as the percent of water
which was transferred into ice during the freezing process. As shown in Figure 3.25, the
freezing degree increased during the freezing process, and it became constant when the
temperature dropped to around -15oC. The dielectric constant decreased with the
temperature dropping down. This is because the free water turned into ice during the
freezing process, and ice has a much smaller dielectric constant than the liquid water.
1.0
0.8
ka Voltage
0.6
0.4
0 min
20 min
40 min
60 min
80 min
100 min
0.2
0.0
-0.2
-0.4
0.0
0.4
0.8
1.2
1.6
Scaled Distance
2.0
Figure 3.24 TDR signals for clay during freezing-thawing process
103
30
Temperature (oC)
20
80
temperature
freeze degree
10
0
Temperature is 0oC
60
40
20
-10
0
-20
0
50
Freezing Degree (%)
100
100 150 200 250
Time (min)
1.0
0.9
Ecb
Ka
Ecb
0.8
0.7
Temperature is 0 oC
0.6
0.5
0.4
0
50
100 150 200 250
Time (min)
22
20
18
16
14
12
10
8
6
4
Ka
Figure 3.25 Temperature curve and freezing degree of the clay during freezing process
Figure 3.26 Dielectric constant (Ka) and electric conductivity (Ecb) during the freezing
process
104
1.0
18
0.9
16
Ecb
0.8
14
12
0.7
10
0.6
Ka
Ecb
Ka
8
0.5
6
0.4
4
-20 -15 -10 -5 0 5 10 15 20 25
Temperature (oC)
Figure 3.27 Dielectric constant (Ka) and electric conductivity (Ecb) during the freezing
process
The thermal pulse responses were shown in fig. 3.28. As the TDR signal is
responsive to the amount of free water, it is a good indicator of the freezing degree in the
partially frozen soil. The thermal pulse responses, in the meanwhile, can be utilized to
determine the thermal conductivity at different freeze-thaw status. The apparent dielectric
constant decreased as the temperature dropped, and it is almost a constant during the
frozen period.
105
100
freeze degree
80
60
40
20
0
-20 -15 -10 -5 0 5 10 15 20 25
temperature
Figure 3.28 Freezing Degree during the freezing-thaw process
Temperature (oC)
30
20
10
0
Heater
Receiver A
Receiver B
Specimen Center
Environmental Temp
-10
-20
0
10000 20000
Time (s)
30000
Figure 3.29 Environment and sensor temperature during the freezing-thaw process
106
Thermal Conductivity (W/(m*K))
3.0
2.5
2.0
1.5
1.0
0.5
-20
-15
-10
-5
0
5
10
15
20
Temperature (oC)
Figure 3.30 Thermal Conductivity of the clay during the freezing-thaw process
The thermal conductivity and volumetric capacity of soil during the freezing
process were shown in Figure 3.31. There is a trend of over estimation of the values of
both the thermal conductivity and volumetric capacity. According to Equation 22 and
Equation 23, this over estimation was induced by the latent heat of fusion for water, and
the amount of the over estimation was based on the physical properties of the soil, such
as volumetric water content, hydraulic conductivity and the matric potential. Usually, the
thermal conductivity and volumetric capacity of the soil will became stable after the
temperature dropped under -10oC, and the soil thermal properties do not change a lot
after the temperature is below -10oC. Thus it is widely accepted to use the soil thermal
properties under -10oC to represent those in a temperature range between -10oC and 0oC.
107
Thermal Conductivity (W/(m*K))
3.0
2.5
2.0
1.5
1.0
0.5
-20
-15
-10
-5
0
5
10
15
20
Temperature (oC)
Figure 3.31 Thermal Conductivity of the clay during the freezing-thaw process
3.4 CONCLUSIONS
This paper describes the fabrication and evaluation of a thermo-TDR sensor for
thermal and moisture related to civil engineering applications. This sensor integrates
conventional TDR probe with Thermal Pulse Technology (TPT) module to measure the
physical and thermal properties of soils nondestructively. Laboratory evaluation indicates
the sensor has good sensitivity in both the TDR and TPT functions. FEM analyses were
performed to study the pattern of heat transfer around the sensor. The sensor geometry
used in previous research was found not optimized. Corrections of the sensor signal were
proposed to account for the deviation of the actual sensor from the theoretical model
assumptions.
The results validated that the signal analyses methods achieved good
accuracy, and the sensor could work efficiently with very limited power supply which
qualifies this thermal-TDR sensor in the long term monitoring service for the civil
engineering materials.
108
109
CHAPTER 4 THE EFFECTS OF NANO-SILICA FUME ON THE
MICROSTRUCTURE AND PERFORMANCE PROPERTIES OF
GYPSUM MORTAR
4.1 INTRODUCTION
With the flourishing development of nano technology, the use of nano-particles,
such as nano silicate fume and titanium dioxide, has received particular attention as
potential additives in cement based materials. Various technologies, such as differential
thermal analysis, Helium inflow, X-ray diffraction, scanning electron microscopy,
nuclear magnetic resonace were used to study the influences of these nano particles on
cement based materials (Qing et al. 2007, Sanchez and Ince 2009, Senff et al. 2009).
Experimental results showed positive effects of the nano particles on modifying the
mechanical properties of the hydrated materials and also decreased the hydration
durations (Jennings 2000, Jo 2007, Li et al. 2004).
Calcium sulphate cement is widely used in pavement recovery construction,
buildings and medical industries. The calcium sulphate cement carries the advantages that
it has much shorter hydration time than the traditional Portland cement. It could be cast
into various shapes after the hydration reaction, and mixed with some polymers, it could
serve as a bone repair cement(Hand 1994; Singh and Middendorf 2007). Therefore, it is
very interesting to study the influences of nano particles on the calcium sulfate cement to
further improve the performance and functionality of this material.
In this paper, experimental programs were conducted to investigate the effects of
nano silicate dioxide on the microstructure and performance properties of the hydrating
110
calcium sulphate cement. Both mechanical properties and microstructures were studied.
Time Domain Reflationary (TDR) and thermal couples were utilized to monitor the
hydrating process of the calcium sulphate cement.
4.2 EXPERIMENTAL PROCEDURE
Raw Materials Analysis
Fine sand and the calcium sulphate cement are the primary components of the
cement mortar. Figure 1 showed the size characteristics of the sand used in this study.
Basically, the diameters of the sand fall into the range between 30μm and 1mm. Figure 2
showed the SEM micrograms of the calcium silicate cement. Energy Dispersive X-ray
(EDX) analysis identified its elemental composition is CaSO4 (Table 1).
a)
b)
Figure 4.1 a) Fine sand used for the cement mixture; b) SEM micrograph of the finesand
111
Figure 4.2 SEM micrograms of the CaSiO4 at different magnitude levels
Figure 4.3 Peak Identification Results of the Cement
TABLE 1 Quantitative Analysis
Element
O -K
Ca-K
S -K
Total
k-ratio (calc.)
0.0943
0.2665
0.2149
ZAF
4.826
1.146
1.115
Atom %
65.33
17.5
17.17
100
Mixing Procedures
112
Element Wt % Wt % Err. (1-Sigma)
45.5
0.4
30.54
0.16
23.96
0.12
100
Table 2 showed the mixed proportions of the calcium sulphate mortar. For the
nano-cement mortar, 3% of the calcium sulphate was substituted with nano SiO2.
TABLE 2 Mix Proportions
Specimen
No.
PM
N3
Water
(g)
455
455
Cement
(g)
910
883
Sand
(g)
2240
2240
Nano-SiO2
(g)
0
27.3
Water-Reducer
(g)
17.5
26.3
Total
(g)
3622.5
3630.6
(Note: PM means plain mortar, and N3 means 3% of nano SiO2 was added.)
During the mixing process, the nano-SiO2 were first stirred with water at a high
speed (120 rpm) for 1 min (high energy is required for the dispersion of nano particles).
Then calcium sulphate cement was added and mixed at medium speed of 80 (rpm) for 30
sec. Sand was gradually added and mixed at medium speed. Superplasticizer was added
and mixed at high mixing speed for 30 sec. The mixture was rested for 1 min and then
mixed for 1 min at high speed (Li, Zhang et al. 2006). The plain mortar was prepared
with similar procedures but without the step of adding nanoparticles.
Specimens of 1 inch diameter were prepared using standard molds, upon
accomplishing the mixing procedures. The hydration was monitored by Time Domain
Reflectometry (TDR) and a temperature monitoring system. A schematic Figure of these
systems was shown in Figure 3. 40 duplicate specimens were prepared for each type of
cement.
113
Figure 4.4 Schematic Figure of TDR and temperature monitoring system
4.3 RESULTS ANALYSIS
4.3.1 Thermal Process
Figure 4.5 shows the measured temperature curve in Plain Mortar and mortar modified
with nano-particles. Detailed analyses of the temperature curve showed nano particles
changed the rate of hydration reactions. .This is possibly due to the fact these nanoparticles act as nucleate site for cement hydration, which has an impact on the hydration
kenetics. The maximum hydration temperature of nano mortar is about 2 degrees lower
than that of the plain mortar, this is possibly due to the fact that 3% the calcium sulphate
cement was replaced by the nano SiO2 which does not take part in the chemical reaction
directly.
114
36
34
PM
NSi
RoomT
32
Temperature (oC)
30
28
26
24
22
20
18
0
50
100
150
200
250
300
Time (min)
Figure 4.5 Temperature process during the hydration process
4.3.2 TDR Signals Analysis
TDR signals are direct indicators of the amount of free water and the conductivity
of cement mortar (Yu et al. 2005a, Yu et al. 2005b). Observation of the monitored
signals show that the moisture content in the calcium sulphate mortar. Figure 4.6 and 4.7
show that as the hydration process evolves, the moisture content in the mortar decreased.
From the thermal process shown in Figure 4.5, the prime hydration process ceased at
around 181 minutes, but the moisture content in the mortar continues to decrease (as
indicated the continuous variations of the TDR signals). This is possibly due to the fact
that hydration continues within the mortar at lower rate and producing heat only
gradually. Evaporation might also account for portion of the moisture loss from the
mortar.
115
0.8
0.6
0.4
0.2
N3-1
0.0
N3-1
N3-4
N3-7
N3-10
N3-13
N3-61
N3-121
N3-181
N3-751
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
0
500
1000
1500
2000
Scale
Figure 4.6 TDR signals of Nano-SiO2 mortar specimen
0.8
0.6
0.4
0.2
PM-1
0.0
PM-1
PM-4
PM-7
PM-10
PM-61
PM-121
PM-751
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
0
500
1000
1500
2000
Scale
Figure 4.7 TDR signals of plain mortar specimen
4.3.3 Ultrasonic Pulse Velocity (UPV) Analysis
Ultrasonic Pulse Velocity test showed that UPV is similar in the plain mortar to
that in the nano mortar, and there is no obvious trend of velocity increase after the first
day of hydration process. This means that the elastic modulus of calcium sulfate cement
did not increase after the first day of hydration. The test implied that the major hydration
116
reactions accomplished within a day. This is consistent with the trend by the temperature
curve.
3280
N3_Velocity (m/s)
PM_Velocity (m/s)
Linear Fit of N3_Velocity (m/s)
Linear Fit of PM_Velocity (m/s)
3260
Wave Velocity (m/s)
3240
3220
3200
3180
3160
3140
3120
Equation
Adj. R-Square
y = a + b*x
-0.10666
0.04337
N3_Velocity (m/s)
Intercept
3123.81572
N3_Velocity (m/s)
Slope
1.19027
1.65361
PM_Velocity (m/s)
Intercept
3153.10107
32.58284
PM_Velocity (m/s)
Slope
2.73706
2.47128
Value
3100
3080
0
5
10
15
20
25
Standard Error
21.80212
30
Time (Day)
Figure 4.8 UPV of Nano-SiO2 mortar and plain mortar
Table 3 UPV of Nano-SiO2 mortar and plain mortar
Day
1
2
3
7
14
28
N3_Velocity (m/s)
3082.3
3104.3
3162.5
3179.1
3130.1
3032.3
PM_Velocity (m/s)
3197.4
3137.7
3121.4
3135.3
3276.3
3200.7
4.3.4 Compression Test Analysis
The results of the compression test are shown in Table 4. It showed that the nano
particles improved the compressive strength of calcium sulfate cement mortar, especially
at the early stage. From table 4, we can also find that the compressive strength of the
calcium sulphate mortar did not increase after 7 days.
117
Table 4 Compression strength of NanoSiO2 mortar and plain mortar
Time
(Day)
7
14
28
Compression Strength
of PM (kips)
2.09
2.85
1.95
Compression Strength
of N3 (kips)
2.745
3.13
2.19
Strengthened
Percentage (%)
31.34
9.6
12.3
4.3.5 SEM Analysis
Figures 9 and 10 show the SEM images of plain mortar versus mortar modified
with nanoparticles. From the micrograms, it can be seen that nano-particle additives
change the shapes of the gypsum crystals. This is the possible structural reason for the
improvement of the compressive strength by use of nano-particles. Conglomerate of
nano-particles were observed in the cement matrix, which indicate imperfect dispersion
of the nano-particles in cement matrix.
An improved procedure for nano-particle
introduction might help further improve the effectiveness of these materials.
Figure 4.9 SEM micrograms of the Nano-SiO2 mortar
118
Figure 4.10 SEM micrograms of plain mortar
4.4 CONCLUSIONS
Laboratory experiments were conducted to investigate the effects of nano-SiO2
particles on the microstructure and performance properties of hydrating calcium sulphate
materials. The hydration process of plain mortar and those with nano-particles were
monitored using the TDR device and thermal couples. From these both the chemical
hydration and moisture content change were obtained non-destructively. Mechanical
testes showed that the nano particles improved the compressive strength of the calcium
sulphate mortar especially at its early stage. SEM images indicated that the introduction
of nano-silica fume significantly changed the microstructure of Calcium Sulfate cement.
The microstructure of cement matrix becomes better defined. However, conglomerate of
nano-particles were observed in the cement matrix, so an improved dispersion procedure
is necessary to further improve the effectiveness of nano materials.
119
4.5 SUMMARY AND FUTURE PLAN
This research aims to develop a simulation model and an advanced instrument to
study concrete multi-scale behaviors.
1) We are continuing the development and refinement of a multiphysical simulation
model for cement and concrete hydration kinetics. The model couples the chemical
reaction theory, diffusion theory and heat transfer theory. The simulation aims to start
from the fundamental chemical reactions for cements to predict the behaviors during
concrete hydration. The effects of the size, shape and distribution of cement particles on
the hydration process are investigated. A method to estimate the hydration development
of the bulk specimen was proposed based on the predicted hydration development of a
single cement particle.
2) A thermo-TDR technology which integrate EM wave and thermal pulse technology is
being developed to measure the thermal properties of concrete. Initial trials of this
technology on soils show promising results. This technology is being further refined for
application in fresh and hardened concrete.
3) An advanced ultrasonic inversion model has been developed to estimate the pore
structure of concrete. With this model, pores of different size scales can be estimated
from the ultrasonic attenuation in different frequency range. The model will provide
important insight on the effects of pores on the performance and intrinsic properties of
concrete. This work is being further refined as the research continues.
120
4) As a way to understand the behaviors of the basic construction unit in concrete, we
looked into the effects of nano-particles on the microstructure and performance properties
of cementitious materials. We found the addition of nano-particles changed the hydration
rate as well as the microstructure of cement. However, conglomerate of nano-particles
were observed in the cement matrix which requires to further improve the dispersion
procedure.
In the new project year, besides further accomplishing the existing research thrust, we
will look into two important areas to complement our current investigation:
1) The diffusion process in the nanoporous cementitious structure. It has been found
through an advanced instrument that a percolation structure exists on the surface of
hydrating cement. It provides passage for water and ions. As the size of the cement grain
reduces, the interaction of water and ions with the channel wall can not be ignored. The
traditional diffusion theory might need to be revisited to describe the transport process.
This task will be formulated under the theme of transport in the nanoporous cemetitious
structure;
2) The link between molecular simulation and continuous multiphysical model. SEM
images have shown hydrated cement consists of different "building blocks" (such as C-SH gel) that is responsible for its microscopic and macroscopic behaviors. With the
continuous model, it is difficult to explicitly account for such micro or nanostructures.
While molecular simulation offers insight on the topography of hydrated cement, it has to
be combined with a continuous model to be computationally efficient. This task will
121
continue exploring the development of linkage between molecular simulation and
multiphysical model.
122
CHAPTER 5 THE EFFECTS OF NANO-SILICA FUME ON THE
MICROSTRUCTURE AND PERFORMANCE PROPERTIES OF
PORTLAND CEMENT
5.1 INTRODUCTION
Concrete is the most widely used man-made material in the world. In the US, the
concrete industry employs more than two million workers(Wikipedia 2009). Significant
amount of research was done to improve the performance and functionality of concrete
materials. With the flourishing development of nanotechnology, the use of nano-particles,
such as nano silicate fume and titanium dioxide, has received particular attention as
potential additives in cement based materials. Various technologies, such as differential
thermal analysis, Helium inflow, X-ray diffraction, scanning electron microscopy,
nuclear magnetic resonance were used to study the influences of these nano particles on
cement based materials(Qing, Zenan et al. 2007; Sanchez and Ince 2009; Senff,
Labrincha et al. 2009). Experimental results showed positive effects of the nano particles
on improving the mechanical properties of the cementitious materials and also increase
the rate of hydration (Monteiro, Kirchheim et al.; Jennings 2000; Li, Xiao et al. 2004; Li,
Zhang et al. 2006; Jo, Kim et al. 2007).
In this paper, experimental programs were conducted to investigate the effects of
nano silicon dioxide on the microstructure and performance properties of the hydrating
Portland cement. Both mechanical properties and microstructures were studied. The
hydration was also monitored by use of an innovative Time Domain Reflationary (TDR)
sensor.
123
5.2 EXPERIMENTAL PROCEDURE
Mixing Materials Analysis
Nano silicon dioxide particles utilized in this experiment were commercial products from
NanoAmor Inc. The detailed technical information about these nano-particles is listed in
table 1.
Table 1 Technical information about the nano silicon oxide
Purity
APS
SSA
Color
Morphology
Bulk density
True density
99+%
80 nm
440 m2/g
white
spherical
0.063 g/cm3
2.2-2.6 g/cm3
The mortar mix design includes the type I Portland cement and fine sand. Figure 1
showed the size characteristics of the sand used in this study. The diameters of fine sand
fall into the range between 30μm and 1mm. Figure 2 showed the SEM micrograms of
the Portland cement. The cement clinkers are amorphous, and the size distribution varies
around several hundred micrometers.
a)
b)
Figure 5.1 a) Fine sand used for the cement mixture; b) SEM micrograph of the finesand
124
Figure 5.2 SEM image of the nano-SiO2 particles at different magnifications
Mixing Procedures
Table 2 showed the mix proportions of the Portland cement mortar used in this study. For
the nano modified cement mortar, 2% (denoted as N2) and 5% (denoted as N5) of the
Portland cement was substituted with nano silicon dioxide.
TABLE 2 Mix Proportions
Specimen No. Water (g) Cement (g) Sand (g) Nano-SiO2 (g) Water-Reducer (g) Total (g)
PM
N2
N5
(Note: PM means
455
910
455
889
455
868
plain mortar, and N2,
2240
0
2240
18.2
2240
45.5
N5 mean 2% and 5% by weight
17.5
3622.5
26.25
3630.13
26.25
3909.71
of nano-SiO2
was added.)
During the mixing processes, the nano-SiO2 particles were first dispersed in water for 2
minutes using a ultrasonic dispersion tank, and then nano-SiO2 were stirred with water at
a high speed (120 rpm) for 1 min (the high speed is used since high energy is required for
the dispersion of nano particles). Portland cement was then added to the mixture and
mixed at a medium speed of around 80 rpm for 30 seconds. Sand was then gradually
added and mixed at medium speed. Superplasticizer was added and mixed at high mixing
speed (120 rpm) for 30 sec. The mixture was rested for 1 min and then mixed for 1 min at
125
high speed (Li, Zhang et al. 2006).
The plain mortar was prepared with similar
procedures but without the step of adding nanoparticles.
Specimens of 2 inch diameter were prepared using standard molds, upon
accomplishing the mixing procedures. The hydration was monitored by Time Domain
Reflectometry (TDR) and temperature monitoring systems. A schematic Figure of these
systems was shown in Figure 3. 40 duplicate specimens were prepared for each type of
cement.
Figure 5.3 Schematic Figure of TDR and temperature monitoring system
5.3 EXPERIMENTS AND RESULTS ANALYSIS
5.3.1 Monitoring of Hydration InducedThermal Process
Figure 4 shows the measured temperature curve in Plain Mortar and mortar
modified with nano-particles. Detailed analyses of the temperature curve showed nano
particles changed the rate of hydration reactions. This is possibly due to the fact these
nano-particles act as nucleate site for cement hydration, which has an impact on the
hydration kinetics. Since the specimens used for monitoring hydration released heat are
126
small ones (2 in by 4 in cylinders), there are only slight increases in the temperature of
the specimens due to hydration (around one degree only). Compared with this, the
variations of the room temperature had a major impact on the observed temperature
curve. This implies a very good temperature controlled chamber is needed to reduce the
effects of environmental temperature on the measured temperature curve. The addition
of nano-sized silica fume, however, causes a more pronounced secondary hydration
reaction, which is shown in the 2nd peak of the temperature curve.
Room T
PM T
Nano 2%
Nano 5%
Temperature (oC)
26
25
24
23
22
1200
1500
1800 2100
Time (s)
2400
Figure 5.4 Temperature process during the hydration process
5.3.2 TDR Signals Analysis
Time Domain Reflectometry (TDR) is a guided wave electromagnetic wave
technology that can be used to study material behaviors.
TDR signals are direct
indicators of the amount of free water and the conductivity of cement mortar (Yu and
Drnevich 2004; Yu 2005). The monitored signals directly reflect the change in the
amount of free water content in the Portland cement mortar. Figure 5, 6 and 7 shows that
as the hydration process evolved, there are systematic changes in the TDR signals. This
indicates the decreases in the free moisture content in the mortar.
127
Relative Voltage (V)
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
PM-1
PM-11
PM-21
PM-151
PM-201
PM-301
PM-1007
0
500
1000
1500
2000
Scaled Distance (m)
Relative Voltage (V)
Figure 5.5 TDR signals of plain mortar specimen (at 1, 11, 21, 151, 201, 301 and 1007
minutes after curing)
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
N2-1
N2-11
N2-21
N2-151
N2-201
N2-301
N2-1007
0
500
1000
1500
2000
Scaled Distance (m)
Figure 5.6 TDR signals of 2% Nano-SiO2 mortar specimen (at 1, 11, 21, 151, 201, 301
and 1007 minutes after curing)
128
Relative Voltage (V)
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
N5-1
N5-11
N5-21
N5-151
N5-201
N5-301
N5-1007
0
500
1000
1500
2000
Scaled Distance (m)
Figure 5.7 TDR signals of 5% Nano-SiO2 mortar specimen (at 1, 11, 21, 151, 201, 301
and 1007 minutes after curing)
5.3.3 Ultrasonic Pulse Velocity (UPV) Analysis
Ultrasonic Pulse Velocity tests were conducted on specimens to measure the
ultrasonic velocities at different curing time. A plot of the measured ultrasonic velocities
is shown in Fig. 8. This Figure indicates that the ultrasonic velocity increases as the
hydration process continues. The velocity is similar (or slightly higher ) in the plain
mortar as that in the nano modified mortar. The ultrasonic test results implied that the
nano-particles additives does not have obviously improvement of the elastic modulus of
the cement mortar.
129
Wave Velocity (m/s)
4200
4000
3800
PM_Velocity (m/s)
N2_Velocity (m/s)
N5_Velocity (m/s)
Linear Fit of PM_Velocity (m/s)
Linear Fit of N2_Velocity (m/s)
Linear Fit of N5_Velocity (m/s)
3600
3400
0
5
10
15
20
25
30
Day
Figure 5.8 UPV of Nano-SiO2 mortar and plain mortar
TABLE 3 UPV of Nano-SiO2 mortar and plain mortar
Day
2
3
5
7
14
28
PM_Velocity (m/s)
3730
3982
4085
4128
4128
4221
N2_Velocity (m/s)
3461
3611
3760
3784
3794
3872
N5_Velocity (m/s)
3352
3653
3836
3924
3924
3902
5.3.4 Results of Compression Strength
Figure 9 and Table 4 present the results of compression strength measured at
different curing ages. The strengths were the average of testing results on 2 or 3 samples.
It can be seen that 2% nanoSiO2 mortar has higher 3-days compressive strength than the
plain mortar. But after that, there is no significant difference between the compressive
strengths of nano-modified mortar versus the plain mortar.
A high nano-SiO2
concentration, there is even a trend where the compressive strength of mortar decreases,
such as observed on the specimens where 5% of nano particles were applied. This might
be caused by a few possible factors. First, the substitution of cement with nano particles
130
slightly reduced the water cement ratio for nano-modified mortar. Secondly, it’s very
difficult to disperse nano particles at high concentrations. It was found the nano particles
tend to cling to each other at high concentration. A weak zone might form due to such
conglomeration. Thirdly, since nano particles have a very large specific surface area,
large amount of air bubbles were found to be attracted in the cement matrix. This was
observed during the mixing process.
The high porosity might have resulted in the
decrease in its mechanical properties for nano-modified mortar at high cencentrations.
TABLE 4 Compression strengths of NanoSiO2 mortar and plain mortar
Time
(Day)
Compressive Strength (kips)
3
5
7
14
28
Compression
Strength
of PM (kips)
8.58
9.89
13.72
13.87
15.08
Compression
Strength
of N2 (kips)
11.07
10.86
12.07
13.38
15.63
17
16
15
14
13
12
11
10
9
8
7
Compression
Strength
of N5 (kips)
7.50
10.45
11.06
11.37
16.25
PM (kips)
N2(Kips)
N5 (kips)
Linear Fit of PM (kips)
Linear Fit of N2(Kips)
Linear Fit of N5 (kips)
0
5
10
15
20
25
30
Day
Figure 5.9 Compressive strengths of Nano-SiO2 mortar and plain mortar
Figure 10 plotted the measured ultrasonic velocity versus the compressive
strength of mortar specimens. The Figure showed that the higher the UPV in the mortar,
the higher of the compressive strength. For specimens of the similar compressive
131
strength, those treated with nano-silica dioxide particles tend to have lower wave
Compression Strength (kips)
velocity.
PM-Compression Strength(kips)
N2-Compression Strength(kips)
N5-Compression Strength(kips)
Linear Fit of PM-Compression Strength(kips)
Linear Fit of N2-Compression Strength(kips)
Linear Fit of N5-Compression Strength(kips)
17
16
15
14
13
12
11
10
9
8
7
3600 3700 3800 3900 4000 4100 4200
Wave Velocity (m/s)
Figure 5.10 Compressive strengths versus UPV of Nano-SiO2 mortar and plain mortar
5.3.5 Splitting Test Analysis
Splitting tests were conducted to measure the tensile strength of the mortar
specimens. The results were summarized in Table 5 as well as Figs. 11 and 12. Similar
observations were observed from the splitting tests results as with the compression tests.
High concentration of nano-particles caused the reduction in the splitting strength of the
mortar samples. The main reason for this phenomenon is that some weak zones might
have formed in nano-silica fume modified mortar due to the conglomeration of the nano
silicon dioxide particles. These weak zones became the structural defects that might lead
to easier damage initialization.
132
TABLE 5 Splitting strengths of NanoSiO2 mortar and plain mortar
Time (Day)
3
5
7
14
28
Splitting Strength
of PM (kips)
12.37
11.76
14.00
13.76
15.11
Splitting Strength
of N2 (kips)
10.97
11.92
12.54
13.89
12.31
Splitting Strength
of N5 (kips)
8.99
10.89
12.55
12.44
13.86
Splitting Strength (kips)
15
14
13
12
11
PM (kips)
N2(Kips)
N5 (kips)
Linear Fit of PM (kips)
Linear Fit of N2(Kips)
Linear Fit of N5 (kips)
10
9
0
5
10
15
20
25
30
Day
Figure 5.11 UPV of Nano-SiO2 mortar and plain mortar
Splitting Strength (kips)
15
14
13
12
11
PM-Splitting Strength(kips)
N2-Splitting Strength(kips)
N5-Splitting Strength(kips)
Linear Fit of PM-Splitting Strength(kips)
Linear Fit of N2-Splitting Strength(kips)
Linear Fit of N5-Splitting Strength(kips)
10
9
3600 3700 3800 3900 4000 4100 4200
Wave Velocity (m/s)
Figure 5.12 Splitting strengths versus UPV of Nano-SiO2 mortar and plain mortar
133
5.3.6 Abrasion Test
Abrasion tests were performed on mortar specimens with different curing ages.
The standard 60# sand paper was used in the abrasion tests. The specimens were held
under a surcharge force of 15 lbs. The speed of the circumvolve is 150 r/min and the time
duration is 1 min. The weights of the specimens were measured before and after the
abrasion tests, from this the abrasion rates were calculated.
Among the three types of mortar specimens tested, mortar treated with 2% nanoparticles showed the best abrasion resistance, while those treated with 5% nano mortar
showed the worst abrasion performance.
The reduction in the effectiveness at high
nano-silica fume concentration might be due to the issues such as inadequate dispersion
and conglomeration. This indicates that appropriate proportion and effective dispersion
techniques are important to ensure the nano-silica fume modification is effective in
improving the performance of cement mortar.
TABLE 6 Abrasion resistances of NanoSiO2 mortar and plain mortar
Time (Day)
3
5
7
Abrased Weight
Percentage of PM(%)
18.55
9.14
6.28
Abrased Weight
Percentage of N2(%)
14.83
8.05
4.70
134
Abrased Weight
Percentage of N5(%)
21.30
14.76
8.24
Abrasion Percentage
0.22
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
PM
N2
N5
Linear Fit of N5
Linear Fit of PM
Linear Fit of N2
Equation
y = a + b*x
Adj. R-Square
1
N5
N5
PM
PM
N2
N2
Intercept
Slope
Intercept
Slope
Intercept
Slope
3
0.8265
0.92638
Value
Standard Error
0.31092
1.51841E-4
-0.03265
2.88675E-5
0.26661
0.04973
-0.03068
0.00945
0.21856
0.02604
-0.02532
0.00495
4
5
6
7
Day
Figure 5.13 Abrasion percentages using the lost weight method
5.3.7 SEM Analysis
Figures 14 show the SEM images of plain mortar versus mortar modified with
nanoparticles. From the micrograms, it can be seen that nano-particle additives change
the shapes of the cement mortar crystals. This is the possible structural reason for the
improvement of the abrasion resistances by use of nano-particles. Conglomerate of nanoparticles were observed in the cement matrix, which indicate imperfect dispersion of the
nano-particles in cement matrix. An improved procedure for nano-particle introduction
might help further improve the effectiveness of these materials.
a)
b)
135
c)
d)
Figure 5.14 SEM micrograms of a) plain mortar, b) 2% nano mortar,
c) and d) 5% nano mortar
CONCLUSIONS AND DISCUSSIONS
Laboratory experiments were conducted to investigate the effects of nano-SiO2
particles on the microstructure and performance properties of hydrating cement materials.
The hydration process of plain mortar and those with nano-particles were monitored
using a TDR device and thermal couples. From these the trend of the chemical hydration
and moisture content change were obtained non-destructively.
From the results of ultrasonic test, it was found that the elastic modulus of the
concrete was not improved significantly after the addition of nano silica fume. While the
experimental data on the compressive strength and slip tests shows that addition of 2%
nano SiO2 helped to improve the strength, the tests results also showed that excess nanoSiO2 particles (such as at 5%) weakened the performance properties, i.e., the
compression strength and splitting strength of the mortar. A few possible explanations are
provided to interpret the observed behaviors. Abrasion tests showed that adding 2% of
nano particles in cement resulted in the increase of the abrasion resistances. Since they
can act as the kernels during the hydration process, appropriate amounts of nano silica
136
fume will help the polymerization process during cement hydration by making the
concrete matrix more homogenous. The experimental procedures in the subsequent
investigations will be further improved based on the experience accumulated during this
experimental program.
137
CHAPTER 6 THE EFFECTS OF NANO-TITANIUM FUME ON THE
MICROSTRUCTURE AND PERFORMANCE PROPERTIES OF
PORTLAND CEMENT MORTAR
6.1 EXPERIMENTAL PROCEDURE
Mixing Materials Analysis
The mortar mix design includes the type I Portland cement and fine sand. The
diameters of fine sand fall into the range between 30μm and 1mm. Nano titanium
dioxide particles utilized in this experiment were commercial products from NanoAmor
Inc. The detailed technical information about these nano-particles was listed in table 6.1.
Figure 6.1 showed the microstructure of the Nano-TiO2 particles using TEM
(Transmission electron microscopy).
Table 6.1 Technical information about the nano titanium oxide
Purity
APS
SSA
Color
Morphology
Bulk density
True density
99+%
30-40 nm
30 m2/g
white
spherical
0.4 g/cm3
3.94 g/cm3
(http://nanoamor.com)
Figure 6.1 TEM image for the nano TiO2 particles (http://nanoamor.com)
Mixing Procedures
138
Three groups of laboratory tests were conducted for the Nano-TiO2 modified
concrete, they are plain mortar, cement mortar with 0.5% (weight ratio of nano particles to
cement) Nano-TiO2 and cement mortar with 1% Nano-TiO2. Table 6.2 listed the mix
proportions for this test.
Table 6.2 Mix Proportions
Specimen No. Water (g) Cement (g)
PM
Ti0.5
Ti1
65
65
65
Sand (g)
Nano-TiO2
Water-Reducer
Total
130
130
130
320
0
0.39
515.39
320
0.65
0.52
516.17
320
1.3
0.65
516.95
(Note: PM means plain mortar, and Ti0.5, Ti1 mean 0.5% and 1% by weight of nano-TiO2
was added.)
During the mixing processes, the nano-TiO2 particles were first dispersed in water for 2
minutes using a ultrasonic dispersion tank, and then nano-TiO2 were stirred with water at a high
speed (120 rpm) for 3 min (the high speed is used since high energy is required for the dispersion
of nano particles). Portland cement was then added to the mixture and mixed at a medium speed
of around 80 rpm for 1 minute. Sand was then gradually added and mixed at a medium speed.
Superplasticizer was added and mixed at a high mixing speed (120 rpm) for 30 sec. The mixture
was rested for 1 min and then mixed for 1 min at high speed (Li, Zhang et al. 2006). The plain
mortar was prepared with similar procedures but without the step of adding nanoparticles.
Specimens of 2 inch diameter were prepared using standard molds, upon
accomplishing the mixing procedures. A total of 42 specimens were casted for this test. The
hydration was monitored by Time Domain Reflectometry (TDR) and temperature monitoring
systems.
139
6.2 EXPERIMENTS AND RESULTS
6.2.1 Monitoring of Hydration InducedThermal Process
Figure 6.2 shows the measured temperature curve in Plain Mortar and mortar modified
with nano-particles. Cement mortar with 0.5% Nano-TiO2 has the fastest hydration rate and
released more heat than the other two specimens. While more nano particles will not surely
increase the hydration rate since the nano particles tend to congregate and do not participate in the
chemical reaction directly. Detailed analyses of the temperature curve showed nano particles
changed the rate of hydration reactions. This is possibly due to the fact these nano-particles act as
nucleate site for cement hydration, which has an impact on the hydration kinetics.
Temperature (oC)
32
AIR
PM
Ti0.5
Ti1
30
28
26
24
22
0
1000 2000 3000 4000
Time (s)
Figure 6.2 Temperature monitoring during hydration process
6.2.2 TDR signal analysis
Time Domain Reflectometry (TDR) is a guided wave electromagnetic wave technology
that can be used to study material behaviors. TDR signals are direct indicators of the amount of
140
free water and the conductivity of cement mortar (Yu and Drnevich 2004; Yu 2005). The
monitored signals directly reflect the change in the amount of free water content in the Portland
cement mortar. Figure 6.3, 6.4 and 6.5 shows that as the hydration process evolved, there is a
systematic change in the TDR signals. This indicates the decreases in the free moisture content in
the mortar. Figure 6.6 shows the TDR signals of the plain mortar and nano particles modified
mortar at the same hydration stages. From the decrease of the moisture content, we can see that
cement mortar with 0.5% nano-TiO2 has higher hydration rate than the other two. This conlusion
Relative Voltage
is consistant with what was observed from the thermal analysis.
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
PM-1 min
PM-61 min
PM-121 min
PM-200 min
PM-355 min
PM-751 min
0
500
1000
Scale
1500
2000
Figure 6.3 TDR signals of plain concrete mortar specimen
141
Relative Voltage
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
Ti0.5-1 min
Ti0.5-61 min
Ti0.5-121 min
Ti0.5-200 min
Ti0.5-355 min
Ti0.5-751 min
0
500
1000
Scale
1500
2000
Relative Voltage
Figure 6.4 TDR signals of Nano-TiO2 0.5% mortar specimen
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
Ti1-1 min
Ti1-61 min
Ti1-121 min
Ti1-200 min
Ti1-355 min
Ti1-751 min
0
500
1000
Scale
1500
2000
Figure 6.5 TDR signals of Nano-TiO2 1% mortar specimen
142
PM-1 min
Ti0.5-1 min
Ti1-1 min
Relative Voltage
Relative Voltage
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
0
500
1000
Scale
1500
2000
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
PM-121 min
Ti0.5-121 min
Ti1-121 min
0
500
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
PM-61 min
Ti0.5-61 min
Ti1-61 min
0
500
1000
Scale
1500
2000
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
0
500
Relative Voltage
Relative Voltage
500
1000
Scale
1000
Scale
1500
2000
d)
PM-355 min
Ti0.5-355 min
Ti1-355 min
0
2000
PM-200 min
Ti0.5-200 min
Ti1-200 min
c)
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
1500
b)
Relative Voltage
Relative Voltage
a)
1000
Scale
1500
2000
e)
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
PM-751 min
Ti0.5-751 min
Ti1-751 min
0
500
1000
Scale
1500
2000
f)
Figure 6.6 TDR signals of plain mortar and nano particles modified mortar at different
hydration stages
143
6.2.3 Ultrasonic Pulse Velocity (UPV) Analysis
Ultrasonic Pulse Velocity tests were conducted on specimens to measure the ultrasonic
velocities at different curing time. Figure 6.7 shows an example of the ultrasonic signal collected
in the laboratory test. A plot of the measured ultrasonic velocities is shown in Figure 6.8. This
Figure indicates that the ultrasonic velocity increases as the hydration process continues. The
velocity is similar (or slightly higher) in the plain mortar as that in the nano modified mortar. The
ultrasonic test results implied that the nano-particles additives did not obviously improve the
elastic modulus of the cement mortar.
Figure 6.7 An example of the ultrasonic signal for the cylinda concrete specimen test
144
4600
Wave Velocity (m/s)
4500
4400
4300
4200
4100
4000
PM_Velocity (m/s)
Ti0.5_Velocity (m/s)
Ti1_Velocity (m/s)
3900
3800
0
5
10
15
Day
20
25
30
Figure 6.8 UPV of Nano-TiO2 mortar and plain mortar
Table 6.3 UPV of Nano-TiO2 mortar and plain mortar
Day
3
7
14
28
PM_Velocity (m/s)
4227
4297
4454
4511
Ti0.5_Velocity (m/s)
3813
4009
4087
4121
Ti1_Velocity (m/s)
4121
4272
4410
4574
6.2.4 Results of Compression Strength
Figure 6.9 and Table 6.4 present the results of compression strength measured at different
curing ages. The strengths were the average of testing results on 2 or 3 samples. It can be seen
that 0.5% nanoTiO2 mortar has a higher 3-days compressive strength than the other two mortars.
But after that, there is no significant difference between the compressive strengths of nanomodified mortar versus the plain mortar.
145
Table 6.4 Compression strength of NanoTiO2 mortar and plain mortar
Plain Mortar (Kips)
9.36
10.25
10.84
12.60
Compressive Strength (Kips)
Time (Day)
3
7
14
28
Ti0.5 (Kips)
10.98
11.35
11.67
12.42
Ti1 (Kips)
9.61
10.39
11.42
13.29
Plain Mortor (Kips)
Ti0.5 (Kips)
Ti1 (Kips)
Linear Fit of Plain Mortor (Kips)
Linear Fit of Ti0.5 (Kips)
Linear Fit of Ti1 (Kips)
13
12
11
10
9
0
5
10 15 20
Time (Day)
25
30
Figure 6.9 Compressive Strength of the Nano-TiO2 mortar and plain mortar at different
hydration stages
6.2.5 Splitting Test Analysis
Splitting tests were conducted to measure the tensile strength of the mortar specimens.
The results were summarized in Table 6.5 as well as Figure 6.10. Similar observations were
observed from the splitting tests results as with the compression tests. High concentration of
nano-particles caused the reduction in the splitting strength of the mortar samples. The main
reason for this phenomenon is that some weak zones might have formed in nano-TiO2 fume
modified mortar due to the conglomeration of the nano titanium dioxide particles. These weak
zones became the structural defects that might lead to easier damage initialization.
146
Table 6.5 Tensile strength of NanoTiO2 mortar and plain mortar
Time (Day)
3
7
14
28
Plain Mortar (Kips)
9.85
9.92
12.14
12.60
Ti0.5 (Kips)
10.12
10.91
12.42
13.03
Ti1 (Kips)
9.79
10.42
12.59
12.68
Splitting Strength (Kips)
13
12
11
Plain Mortar (Kips)
Ti0.5 (Kips)
Ti1 (Kips)
Linear Fit of Plain Mortar (Kips)
Linear Fit of Ti0.5 (Kips)
Linear Fit of Ti1 (Kips)
10
0
5
10
15
20
25
30
Time (Day)
Figure 6.10 Splitting Strength of the Nano-TiO2 mortar and plain mortar at different
hydration stages
6.2.6 Abrasion Test
Abrasion tests were performed on mortar specimens with different curing ages. The
standard 60# sand paper was used in the abrasion tests. The specimens were held under a
surcharge force of 15 lbs. The speed of the circumvolve is 150 r/min and the time duration is 1
min. The weights of the specimens were measured before and after the abrasion tests, from this
the abrasion rates were calculated. Figure 6.11 showed the photo of the abrasion test equipment.
Among the three types of mortar specimens tested, the cement mortar treated with 0.5%
nano-particles showed the best abrasion resistance, while those treated with 1% nano mortar
showed the worst abrasion performance. The reduction in the effectiveness at high nano-TiO2
147
fume concentration might be due to the issues such as inadequate dispersion and conglomeration.
This indicates that appropriate proportion and effective dispersion techniques are important to
ensure the nano-silica fume modification is effective in improving the performance of cement
mortar. Also, the nano particles modified procedure could not significantly improve the abrasion
resistance of the cement mortar especially when the hydration completed.
Figure 6.11 Photo of equipment for the abrasion test
Table 0.1
Table 6.6 Abrasion resistance of NanoTiO2 mortar and plain mortar
Time (Day)
3
7
14
28
Plain Mortar (%)
0.1532
0.1198
0.0977
0.0654
Ti0.5 (%)
0.1375
0.1002
0.0793
0.0478
148
Ti1 (%)
0.1426
0.1106
0.0755
0.0536
Abrasion Percentage (%)
13
12
11
Plain Mortar (Kips)
Ti0.5 (Kips)
Ti1 (Kips)
Linear Fit of Plain Mortar (Kips)
Linear Fit of Ti0.5 (Kips)
Linear Fit of Ti1 (Kips)
10
0
5
10 15 20
Time (Day)
25
30
Figure 6.11 Abrasion Percentage of the Nano-TiO2 mortar and plain mortar at different
hydration stages
6.3 MICROSTRUCTURE STUDY FOR ADDITIVE M ODIFIED CONCRETE
A scanning electron microscope equipped with energy dispersive X-ray analysis
(SEM- EDX) is an important supplement to the optical microscope when examining new,
old and deteriorated concrete.
149
6.3.1 Testing Equipment Involved in the Microstructure Study
Figure 6.12 Field-Emission Gun Scanning Electron Microscope Hitachi
S4500(“http://dmseg5.case.edu/Groups/ernst/scsam.html”)
This scanning electron microscope is equipped with a field-emission gun, two
secondary electron detectors, a backscatter detector, and an infrared chamber scope. In
addition, it has a Noran XEDS (X-ray energy-dispersive spectrometry) system. The
microscope is capable of operating at a spatial resolution of less than 1.5 nm at 15 keV
energy. It also performs well at reduced beam energies (1 kV), facilitating the
observation of highly insulating materials and of producing micrographs that emphasize
the near-surface structure of the specimen.
150
Figure 6.13 Dual Beam Focused Ion Beam System Fei Xt Nova Nanolab
200(“http://dmseg5.case.edu/Groups/ernst/scsam.html”)
As part of a "Wright Center for Innovation," SCSAM has installed a dual beam
FIB (focused ion beam) system of the type xT Nova Nanolab 200 (FEI). In addition to
the focused ion beam, which is used for machining thin foils suitable for TEM directly
out of the specimen surface, this instrument includes a complete and very-high-quality
scanning electron microscope. This system has the advantage that the specimen can be
observed by (high-resolution) SEM while being milled by the ion beam. Compared to
previous FEI FIB systems, the Nova comes with a newly designed computer interface
and software that enables entirely automated milling. Moreover, the Nova includes a
newly designed internal "lift-out-" system for transferring the thin film generated by ionbeam milling onto a special kind of Cu support grid, which can then be loaded into the
specimen holder of a TEM.
151
For elemental analysis, the system is equipped with a state-of-the-art XEDS
XFlash detector 4010 system by Bruker. At the heart of it the system contains a Si-drifted
detector, which has been specially selected to provide an outstanding energy resolution
with a FWHM (full width at half maximum) of 125eV for Mn Kα (5.899keV) at a
throughput of 60..90kcps (kilo counts per second). This detector is also suited for the
detection of light elements. At its highest throughput the system is capable of counting up
to 275kcps.
Due to the special chip design with the integrated charge amplifier, the XFlash
can process extremely high count rates and at the same time displays a very good energy
resolution, unrivalled by any other energy dispersive X-ray detectors. This is due to a
monolithically integrated on-chip FET acting as a signal amplifier and supports
unprecedented energy resolution. The detector has an active area of 10mm2 and is cooled
by a Peltier element. It has a super light element window (SLEW), allowing the detection
of boron and heavier elements.
A state-of-the-art Nordlys II EBSD Detector serves for EBSD (electron
backscatter diffraction) and related techniques. EBSD measurements of phase and
orientation rely on detecting and analyzing electron backscatter (Kikuchi) patterns
(EBSP) generated in the SEM from a polycrystalline sample. NordlysS achieves the high
sensitivity and CCD resolution. At its highest resolution setting, the full 1344×1024×12
bit pixel CCD array is used to digitize EBSP's offering a direct benefit for phase
identification and discrimination of materials with very similar crystallographic lattice
parameters as well as accurate measurement of orientation in materials with close c/a
152
ratios, where pseudo symmetry can thwart accurate orientation measurements.
Combining NordlysS and Advanced Fit software enables reliable determination of
orientations down to 0.5° and correct identification of orientations in pseudo-symmetric
materials with c/a ratios as low as 2%.
6.4 RESULT ANALYSIS
Figure 6.14 – 6.16 showed the chemical component contour of nano particles
modified concrete. The contour displayed the Calcium, Silicon and Oxygen are the major
chemical components of the concrete. The nano-TiO2 was uniformly distributed into the
cement mortar, and it validated the efficiency of the mixing procedure (Figure 6.16).
Figure 6.17 – 6.19 showed the crack zone of nano particles modified concrete at
different scales. Ca(OH)2 crystals which are of needle shape can usually be found near
the crack zone. These crystals are usually blamed for attenuating the strength of the
concrete structure.
153
Figure 6.14 SEM image and chemical component contour of nano particles modified
concrete (Oxygen and Sodium)
154
Figure 6.15 SEM image and chemical component contour of nano particles modified
concrete (Magnesium, Aluminum and Silicon)
155
Figure 6.16 SEM image and chemical component contour of nano particles modified
concrete (Sulfur, Potassium and Calcium)
156
Figure 6.17 SEM image and chemical component contour of nano particles modified
concrete (Titanium and Iron)
157
a)
b)
Figure 6.18 SEM image for crack zone of nano particles modified concrete, Figure b is
the zoom in Figure for the area in the white block in Figure a
158
Figure 6.19 SEM image for crack zone of nano particles modified concrete
159
Figure 6.20 SEM image for surface conformation of nano particles modified concrete
6.5 CONCLUSIONS
1. The mixing procedure is of primary importance for the mechanical properties of
the nano concrete. The SEM analysis validated the mix procedure applied in this
laboratory test.
2. From the ultrasonic test, we can see that the elastic modulus of the early stage
concrete was not improved significantly due to the nano particles. Nor did that
from the compression and the splitting tests. And there is even a trend that the
nano-TiO2 particles weakened the compression and splitting strength of the
160
concrete, especially when 1% of nano particles were applied. There are several
possible explanations. First, the nano particles substituted part amount of the
cement, so the actual water cement ratio increased. Secondly, it’s very difficult to
disperse large amount of nano particles within limited solution, so the nano
particles clung to each other, and a weak zone was formed due to this
agglomeration. Thirdly, nano particles have very high specific surface area, so
large amount of air bubbles would form during the mixing process. And too many
pores in the concrete will definitely decrease its mechanical properties.
3. Abrasion tests showed that 0.5% of nano particles could increase the abrasion
resistances of the early stage concrete. Nano particles could be developed as the
kernels during the hydration precess. Appropriate content of nano particles could
help the crystallization process and make the concrete matrix more homogenous
and well controlled.
4. SEM and EDX analysis are sound approaches for studying the microstructure of
the concrete structures. It could show the distribution of the chemical components
micro cracks in the concrete. It serves as a key to build a connection between the
microstructure and the macrostructure of the concrete materials.
6.6 FUTURE WORK
1. Other nano particles could be tried to pick out the best chemical objects for the
hydration of the cement.
2. Shaking table could be utilized to reduce the air bubbles after the mixing process.
161
3. Further analysis of the SEM micrograms should be conducted to study and the
microstructure development in the concrete and its correlation with the macro
mechanical performances.
162
CHAPTER 7 CONCLUSIONS AND FUTURE WORK
7.1 CONCLUSIONS
7.1.1 Multi-Physical Simulation for Concrete Hydration Kinetics
A multi-physics numerical model was developed to predict the development of
cement paste hydration. The chemical reaction theory, heat transfer theory and diffusion
theory were coupled in this model. The simulation results were validated based on field
test phenomenon and experiential equations, and promising results were achieved.
Besides predicting the development of the hydration process, this model also proposed a
microstructure based approach to relate the chemical reactions to the strength of cement
paste. Current results showed that this numerical model can help predict early stage
concrete behaviors.
This multi-physics model has many advantages over the traditional models
especially in describing all the three physical fields. And meanwhile this model has great
potential in studying the basic principles of the microstructures for the cement based
materials.
7.1.2 Sensor Technologies to Characterize the Hydration Kinetics
An innovative Thermo-TDR sensor was developed to measure the physical,
thermal and other transport properties of cement based materials and geomaterials. This
sensor integrates the conventional TDR probe with the heat pulse measurement system.
It can be used to collect both the TDR signals and thermal signals at the same time. From
these signals, both the physical properties and thermal properties can be determined.
The technology features the advantages of being multifunctional, sensitive, inexpensive,
163
rugged and easily deployed. The performance was evaluated in laboratory experiments
and demonstrated promising results. This technology can be used to study a variety of
thermal and transport phenomena in concrete. The refined system will help investigate
the thermal and transport properties of concrete in relationship to its durability.
7.1.3 Nano-Particles Additives on the Microstructure and Durability of Concrete
With the assistance of simulation and characterization tools developed from this
project, experimental studies were carried out to evaluate the effects of mineral nanoparticles on the microstructure of concrete. The final goal was set to understand such
interactions and durability mechanism at the lowest structural levels. It was found that
additions of nano-particles changed the hydration rate as well as the microstructure of
cement. However, conglomerate of nano-particles were observed in the cement matrix
which requires a further improvement for the dispersion procedures.
7.2 FUTURE WORK
1. For the muti-physics simulation model, a significant amount of calculation time is
needed for this simulation to process the coupled kinetic hydration development,
so it’s not easy to conduct large scale simulation using a PC. An optimized
algorithm is a possible solution to relieve this pain, also the dramatically
expanding computer technology will be a solution in the near future.
2. Finite element methods were employed in this simulation, so algorithm
convergence problems may arise especially when dealing with the multi-scale
problems.
164
3. For the thermal-TDR sensor, an optimized algorithm and its corresponding
software should be developed for the automatic analysis of the testing signal. It
will also be an important foundation of the smart structure and smart system.
4. Nano technology and microstructure studies will be an essential aspect for the
construction materials research. The construction techniques such as mixing
procedures etc. will play an important role in taking full advantage of this
technology.
5. Besides the mechanical properties, the environmental factors can be studies for
the nano additives regarding the development of a green and sustainable
infrastructure system.
165
REFERENCE
Andrade, C., J. Sarr, et al. (1999). "Relative humidity in the interior of concrete exposed
to natural and artificial weathering." Cement and Concrete Research 29(8): 12491259.
Bentz, D. P. (1997). "Three-dimensional computer simulation of portland cement
hydration and microstructure development." Journal of the American Ceramic
Society 80(1): 3-21.
Bentz, D. P. (2008). "A review of early-age properties of cement-based materials."
Cement and Concrete Research 38(2): 196-204.
Bentz, D. P. (In Press). "Transient plane source measurements of the thermal previous
termpropertiesnext term of hydrating cement pastes." Mater. Struct.
Bentz, D. P., M. R. Geiker, et al. (2001). "Shrinkage-reducing admixtures and early-age
desiccation in cement pastes and mortars." Cement and Concrete Research 31(7):
1075-1085.
Bentz, D. P., O. M. Jensen, et al. (2001). "Influence of Cement Particle-Size Distribution
on Early Age Autogenous Strains and Stresses in Cement-Based Materials."
Journal of the American Ceramic Society 84(1): 129-135.
Drnevich, V. P., Siddiqui, S.I., Lovell, J., and Yi, Q. (2001). Water Content And Density
Of Soil Insitu by the Purdue TDR Method. TDR 2001: Innovative Applications of
TDR Technology, Northwestern University, Evanston, IL.
166
G. J. Kluitenberg, J. M. H., and K. L. Bristow (1993). "Error Analysis of the Heat Pulse
Method for Measuring Soil Volumetric Heat Capacity." Soil Sci. Soc. Am. J. 57:
1444-1451.
Hand, R. J. (1994). "The kinetics of hydration of calcium sulphate hemihydrate: A critical
comparison of the models in the literature." Cement and Concrete Research 24(5):
885-895.
Heitman J.L., R. H., T. Ren, and T.E. Ochsne. (2007). "An improved approach for
measurement of coupled heat and water transfer in soil cells." Soil Sci. Soc. Am. J
71: 872–880.
Jennings, H. M. (2000). "A model for the microstructure of calcium silicate hydrate in
cement paste." Cement and Concrete Research 30(1): 101-116.
Jo, B.-W., C.-H. Kim, et al. (2007). "Characteristics of cement mortar with nano-SiO2
particles." Construction and Building Materials 21(6): 1351-1355.
Keith L. Bristow, G. J. K., and Robert Horton (1994). "Measurement of Soil Thermal
Properties with a Dual-Probe Heat-Pulse Technique." Soil Sci. Soc. Am. J. 58:
1288-1294.
Li, H., H.-g. Xiao, et al. (2004). "Microstructure of cement mortar with nano-particles."
Composites Part B: Engineering 35(2): 185-189.
Li, H., M.-h. Zhang, et al. (2006). "Abrasion resistance of concrete containing nanoparticles for pavement." Wear 260(11-12): 1262-1266.
167
Monteiro, P. J. M., A. P. Kirchheim, et al. "Characterizing the nano and micro structure
of concrete to improve its durability." Cement and Concrete Composites In Press,
Corrected Proof.
Qing, Y., Z. Zenan, et al. (2007). "Influence of nano-SiO2 addition on properties of
hardened cement paste as compared with silica fume." Construction and Building
Materials 21(3): 539-545.
Sanchez, F. and C. Ince (2009). "Microstructure and macroscopic properties of hybrid
carbon nanofiber/silica fume cement composites." Composites Science and
Technology 69(7-8): 1310-1318.
Senff, L., J. A. Labrincha, et al. (2009). "Effect of nano-silica on rheology and fresh
properties of cement pastes and mortars." Construction and Building Materials
23(7): 2487-2491.
Singh, N. B. and B. Middendorf (2007). "Calcium sulphate hemihydrate hydration
leading to gypsum crystallization." Progress in Crystal Growth and
Characterization of Materials 53(1): 57-77.
Wikipedia (2009). Concrete, Wikipedia.
Yu, X. and V. P. Drnevich (2004). Soil Water Content and Dry Density by Time Domain
Reflectometry, ASCE. 130: 922-934.
Yu, X., Drnevich, VP (2005). "Density Compensation of TDR Calibration for
Geotechnical Applications." Journal of ASTM International 2(1): 16.
168
ASTM D6565 (2005). “Standard Test Method for Determination of Water (Moisture)
Content of Soil by the Time-Domain Reflectometry (TDR) Method”. Annual
Book of ASTM Standards, Vol. 04, American Society of Testing and
Materials.
ASTM D6780 (2005). “Standard Test Method for Water Content and Density of Soil
in Place by Time Domain Reflectometry (TDR)”. Annual Book of ASTM
Standards, Vol. 04, American Society of Testing and Materials.
Baker, J. M. and Allmaras, R. (1990). “System for Automating and Multiplexing Soil
Moisture Measurement by Time-Domain Reflectometry”. Soil Sci. Soc. Am.
J., Vol. 55, pp. 1-6.
Benson, C.H., “Plenary Lecture: Geotechnical Applications of TDR", Proc. TDR
2006, Purdue University, West Lafayette, USA, Sept. 2006, Paper ID A3, 1 p.,
https://engineering.purdue.edu/TDR/Papers
Bristow, K.L., G. J. K., and Robert Horton (1994). “Measurement of Soil Thermal
Properties with a Dual-Probe Heat-Pulse Technique”. Soil Sci. Soc. Am. J.
58: 1288-1294.
Campbell, G.S., C. Calissendorff, and J.H. Williams. (1991). “Probe for Measuring
Soil Specific Heat Using A Heat-Pulse Method”. Soil Sci. Soc. Am. J.,
Vol.55, pp.291-293.
Civan, F. (2000). "Unfrozen Water in Freezing and Thawing Soils: Kinetics and
Correlation." Journal of Cold Regions Engineering 14(3): 146-156.
169
de Vries, D.A., “A nonstationary method for determining thermal conductivity of soil
in situ”. Soil Sci. 73 (1952), pp. 83–89.
Drnevich, V. P., Lin, C. P., Yi, Q., Yu, X., and Lovell J. (2001a). “Realtime
determination of soil type, water content and density using electromagnetic”.
Joint Transportation Research Program, Indiana Department of
Transportation-Purdue University.
Drnevich, V. P., Siddiqui, S.I., Lovell, J., and Yi, Q. (2001b). “Water Content and
Density of Soil Insitu by the Purdue TDR Method”. TDR 2001: Innovative
Applications of TDR Technology, Northwestern University, Evanston, IL.
Feng, W., Lin, C. P., Deschamps, R. J., Drnevich, V. P. (1999). “Theoretical Model
of Multisection Time Domain Reflectometry Measurement System”. Water
Resource Research, Vol. 35, No. 8, pp. 2321-2331.
Heitman J.L., R. H., T. Ren, and T.E. Ochsne. (2007). “An improved approach for
measurement of coupled heat and water transfer in soil cells”. Soil Sci. Soc.
Am. J 71: 872–880.
Heitman J.L., R. Horton, Ren T., and Oschsner, T.E. (2007). “An Improved Approach
for Measurement of Coupled Heat and Water Transfer in Soil Cells”. Soil Sci.
Soc. Am. J., Vol. 71, No. 3, pp872-880.
Hotz, R. (2009). “Investigation of the Thermal Conductivity of Compacted Silts and
Its Correlation to the Elastic Modulus”. Journal of Materials in Civil
Engineering.
Liang, R., K. A.-A., Samer Rabab'ah (2006). “Field Monitoring of Moisture
170
Variations Under Flexible Pavement”. TRB 2006 Annual Meeting,
Washington DC.
Kluitenberg, G. J., J. M. H., and K. L. Bristow (1993). “Error Analysis of the Heat
Pulse Method for Measuring Soil Volumetric Heat Capacity”. Soil Sci. Soc.
Am. J. 57: 1444-1451.
Kluitenberg, G.J. , K.L. Bristow and B.S. Das, “Error analysis of heat pulse method
for measuring soil heat capacity, diffusivity, and conductivity”. Soil Sci. Soc.
Am. J. 59 (1995), pp. 719–726.
Lackner, R., A. Amon, et al. (2005). “Artificial Ground Freezing of Fully Saturated
Soil: Thermal Problem”. Journal of Engineering Mechanics 131(2): 211-220.
Lin, C. P. (1999), “Time Domain Reflectometry for Soil Properties”, Ph.D. Thesis,
School of Civil Engineering, Purdue University, West Lafayette, IN.
McGaw, R. (1969). “Heat conduction in saturated granular materials. Special Rept.
103, U.S. Army Cops Engineers”. Cold Regions Research and Engineering
Laboratory, Hanover, New Hampshire. 131p.
Mortensen, A, P., J. W. Hopmans, Y. Mori, and J. Šimůnek, Inverse modeling of
water, heat, and solute transport in the vadose zone using data collection with
a multi-functional heat pulse technique, Advances in Water Resources, 29,
250-267, 2006. Naidu, A. D. and D. N. Singh (2004). "Field Probe for
Measuring Thermal Resistivity of Soils." Journal of Geotechnical and
Geoenvironmental Engineering 130(2): 213-216.
Noborio, K., K.J. McInnes, and J.L. Heilman. 1996. Measurements of soil water
171
content, heat capacity, and thermal conductivity with a single TDR probe. Soil
Sci. 161:22-28.
O'Connor, K. and Dowding, C (1999). GeoMeasurements by Pulsing TDR Cables and
Probes, Boca Raton, CRC Press LLC.
Overduin, P. P., D. L. Kane, et al. (2006). “Measuring thermal conductivity in
freezing and thawing soil using the soil temperature response to heating”.
Cold Regions Science and Technology 45(1): 8-22.
Ramo, S., Winnery, J.R. and VanDuzer, T. (1994). Fields and Waves in
Communications Electronics, John Wiley & Sons.
Ren, T., K. Noborio, and R. Horton (1999). “Measuring Soil Water Content,
Electrical Conductivity, and Thermal Properties with A Thermo-Time Domain
Reflectometry Probe”. Soil Sci. Soc. Am. J., Vol. 63, pp450-457.
Ren, T., T.E. Oshsner and R. Horton (2003). “Development of Thermo-Time Domain
Reflectometry for Vadose Zone Measurements”. Vadose Zone J., Vol. 2,
pp544-551.
Robinson, D. A., C. S. Campbell, J. W. Hopmans, B. K. Hornbuckle, S. B. Jones, R.
Knight, F. Ogden, J. Selker, and O. Wendroth (2008). “Soil Moisture
Measurement for Ecological and Hydrological Watershed-Scale Observations:
A Review”. Vadose Zone J., February 25, 2008; 7(1): 358 - 389.
172
Song, W.-K. (2006). “Thermal Transfer Analysis of a Freezing Soil Medium with an
Embedded Pipeline”. Journal of Cold Regions Engineering 20(1): 20-36.
Saito, H., J. Šimůnek, J. W. Hopmans, and A. Tuli, Numerical evaluation of the heat
pulse probe for simultaneous estimation of water fluxes and soil hydraulic and
thermal properties, Water Resour. Res., 43, W07408,
doi:10.1029/2006WR005320, 2007.
S.M. Welch, G.J. Kluitenberg and K.L. Bristow, Rapid numerical estimation of soil
thermal properties for a broad class of heat pulse emitter geometries. Meas.
Sci. Technol. 7 (1996), pp. 932–938.
Takeda, K. and Y. Nakano (1990). “Quasi-steady problems in freezing soils: II.
Experiment on the steady growth of an ice layer”. Cold Regions Science and
Technology 18(3): 225-247.
Tarnawski, V. R. and B. Wagner (1993). “Modeling the thermal conductivity of
frozen soils”. Cold Regions Science and Technology 22(1): 19-31.
Timlin, D.J. and Pachepsky, Y.A. (1996). “Comparison of Three Methods to Obtain
the Apparent Dielectric Constant from Time Domain Reflectometry Wave
Traces”. Soil Sci. Soc. Am. J. , Vol. 60, pp. 970-977.
Topp, G. C., Davis, J. L., and Annan, A. P. (1980), “Electromagnetic Determination
of Soil Water Content and Electrical Conductivity Measurement Using Time
Domain Reflectometry,” Water Resources Research, Vol. 16, pp. 574-582.
173
Topp, G. C., Davis, J. L., Annan, A. P. (1982). “Electromagnetic Determination of
Soil Water Content Using TDR: II. Evaluation of Installation and
Configuration of Parallel Transmission Lines”. Soil Sci. Soc. Am. J., Vol. 46,
pp. 678-684.
Weon-Keun, S. (2006). “Thermal Transfer Analysis of a Freezing Soil Medium with
an Embedded Pipeline”. Journal of Cold Regions Engineering 20(1): 20-36.
Woodside, W., and J.H. Messmer. (1961). “Thermal conductivity of porous media: I.
Unconsolidated sands”. J. Appl. Phys. 32: 1688-1698.
Yanuka, M., Topp, G.C., Zegelin, S., and Zebchuk, W.D. (1988). “Multiple
Reflection and Attenuation of Time-Domain Reflectometry Pulses:
Theoretical Considerations for Application to Soil and Water”. Water
Resource Research, Vol. 7, pp. 939-944.
Yu, X. and Drnevich V.P. (2004). “Soil Water Content and Dry Density by Time
Domain Reflectometry”. Journal of Geotechnical and Geoenvironmental
Engineering 130(9): 922-934.
Yu, X. (2003). “Influence of Soil Properties and Environmental Conditions on The
Propagation of EM Waves in Soils”. Ph.D. Dissertation, Purdue University,
School of Civil Engineering.
Yu, X.B. and Yu, X. (2006). “Time Domain Reflectometry Tests of Multilayered
Soils”. Proc. TDR 2006, Purdue University, West Lafayette.
Zhou, W. and S. L. Huang (2004). “Modeling Impacts of Thaw Lakes to Ground
Thermal Regime in Northern Alaska”. Journal of Cold Regions Engineering
18(2): 70-87.
174
Qing, Y., et al., Influence of nano-SiO2 addition on properties of hardened cement paste
as compared with silica fume. Construction and Building Materials, 2007. 21(3):
p. 539-545.
Sanchez, F. and C. Ince, Microstructure and macroscopic properties of hybrid carbon
nanofiber/silica fume cement composites. Composites Science and Technology,
2009. 69(7-8): p. 1310-1318.
Senff, L., et al., Effect of nano-silica on rheology and fresh properties of cement pastes
and mortars. Construction and Building Materials, 2009. 23(7): p. 2487-2491.
Jennings, H.M., A model for the microstructure of calcium silicate hydrate in cement
paste. Cement and Concrete Research, 2000. 30(1): p. 101-116.
Jo, B.-W., et al., Characteristics of cement mortar with nano-SiO2 particles. Construction
and Building Materials, 2007. 21(6): p. 1351-1355.
Li, H., et al., Microstructure of cement mortar with nano-particles. Composites Part B:
Engineering, 2004. 35(2): p. 185-189.
Li, H., M.-h. Zhang, and J.-p. Ou, Abrasion resistance of concrete containing nanoparticles for pavement. Wear, 2006. 260(11-12): p. 1262-1266.
Monteiro, P.J.M., et al., Characterizing the nano and micro structure of concrete to
improve its durability. Cement and Concrete Composites. In Press, Corrected
Proof.
175
Hand, R.J., The kinetics of hydration of calcium sulphate hemihydrate: A critical
comparison of the models in the literature. Cement and Concrete Research, 1994.
24(5): p. 885-895.
Singh, N.B. and B. Middendorf, Calcium sulphate hemihydrate hydration leading to
gypsum crystallization. Progress in Crystal Growth and Characterization of
Materials, 2007. 53(1): p. 57-77.
.W. Bullard, M. D. A., Z. Grasley, W. Hansen, N. Kidner, D. Lange, P. Lura, T.O.
Mason, J. Moon, F. Rajabipour, G. Sant, S. Shah, T. Voight, S. Wanson, J. Weiss
and L. Woo (2006). Anext term comparison of test methods for previous
termearly-agenext term behavior of cementitious materials. Second Edition of the
International Symposium on Advances in Concrete Through Science and
Engineering, Quebec.
Al-Qadi, I. L., S. M. Riad, et al. (1997). "Design and evaluation of a coaxial transmission
line fixture to characterize portland cement concrete." Construction and Building
Materials 11(3): 163-173.
Andrade, C., J. Sarr, et al. (1999). "Relative humidity in the interior of concrete exposed
to natural and artificial weathering." Cement and Concrete Research 29(8): 12491259.
Bentz, D. P. (1997). "Three-dimensional computer simulation of portland cement
hydration and microstructure development." Journal of the American Ceramic
Society 80(1): 3-21.
176
Bentz, D. P. (2008). "A review of early-age properties of cement-based materials."
Cement and Concrete Research 38(2): 196-204.
Bentz, D. P. (In Press). "Transient plane source measurements of the thermal previous
termpropertiesnext term of hydrating cement pastes." Mater. Struct.
Bentz, D. P., M. R. Geiker, et al. (2001). "Shrinkage-reducing admixtures and early-age
desiccation in cement pastes and mortars." Cement and Concrete Research 31(7):
1075-1085.
Bentz, D. P., O. M. Jensen, et al. (2001). "Influence of Cement Particle-Size Distribution
on Early Age Autogenous Strains and Stresses in Cement-Based Materials."
Journal of the American Ceramic Society 84(1): 129-135.
Chotard, T. J., A. Smith, et al. (2003). "Characterisation of early stage calcium aluminate
cement hydration by combination of non-destructive techniques: acoustic
emission and X-ray tomography." Journal of the European Ceramic Society
23(13): 2211-2223.
D'Angelo, R., T. J. Plona, et al. (1995). "Ultrasonic measurements on hydrating cement
slurries : Onset of shear wave propagation." Advanced Cement Based Materials
2(1): 8-14.
Drnevich, V. P., Siddiqui, S.I., Lovell, J., and Yi, Q. (2001). Water Content And Density
Of Soil Insitu by the Purdue TDR Method. TDR 2001: Innovative Applications of
TDR Technology, Northwestern University, Evanston, IL.
177
Erdogan, S. T., P. N. Quiroga, et al. (2006). "Three-dimensional shape analysis of coarse
aggregates: New techniques for and preliminary results on several different coarse
aggregates and reference rocks." Cement and Concrete Research 36(9): 16191627.
Feng, X., E. J. Garboczi, et al. (2004). "Estimation of the degree of hydration of blended
cement pastes by a scanning electron microscope point-counting procedure."
Cement and Concrete Research 34(10): 1787-1793.
G. J. Kluitenberg, J. M. H., and K. L. Bristow (1993). "Error Analysis of the Heat Pulse
Method for Measuring Soil Volumetric Heat Capacity." Soil Sci. Soc. Am. J. 57:
1444-1451.
Grasley, Z., D. Lange, et al. (2006). "Internal relative humidity and drying stress
gradients in concrete." Materials and Structures 39(9): 901-909.
Hand, R. J. (1994). "The kinetics of hydration of calcium sulphate hemihydrate: A critical
comparison of the models in the literature." Cement and Concrete Research 24(5):
885-895.
Heitman J.L., R. H., T. Ren, and T.E. Ochsne. (2007). "An improved approach for
measurement of coupled heat and water transfer in soil cells." Soil Sci. Soc. Am. J
71: 872–880.
Jennings, H. M. (2000). "A model for the microstructure of calcium silicate hydrate in
cement paste." Cement and Concrete Research 30(1): 101-116.
178
Jensen, O. M. (1990). The pozzolanic reaction of silica fume, Technical University of
Denmark. M.S. Thesis.
Jensen, O. M., P. F. Hansen, et al. (1999). "Clinker mineral hydration at reduced relative
humidities." Cement and Concrete Research 29(9): 1505-1512.
Jiang, S. P., J. C. Mutin, et al. (1995). "Studies on mechanism and physico-chemical
parameters at the origin of the cement setting. I. The fundamental processes
involved during the cement setting." Cement and Concrete Research 25(4): 779789.
Jo, B.-W., C.-H. Kim, et al. (2007). "Characteristics of cement mortar with nano-SiO2
particles." Construction and Building Materials 21(6): 1351-1355.
K. Maekawa, R. C. a. T. K. (1999). Modelling of Concrete Performance: Hydration,
Microstructure Formation, and Mass Transport. London, E&FN Spon.
Keith L. Bristow, G. J. K., and Robert Horton (1994). "Measurement of Soil Thermal
Properties with a Dual-Probe Heat-Pulse Technique." Soil Sci. Soc. Am. J. 58:
1288-1294.
Li, H., H.-g. Xiao, et al. (2004). "Microstructure of cement mortar with nano-particles."
Composites Part B: Engineering 35(2): 185-189.
Li, H., M.-h. Zhang, et al. (2006). "Abrasion resistance of concrete containing nanoparticles for pavement." Wear 260(11-12): 1262-1266.
179
Lin, F. and C. Meyer (2009). "Hydration kinetics modeling of Portland cement
considering the effects of curing temperature and applied pressure." Cement and
Concrete Research 39(4): 255-265.
Monteiro, P. J. M., A. P. Kirchheim, et al. "Characterizing the nano and micro structure
of concrete to improve its durability." Cement and Concrete Composites In Press,
Corrected Proof.
P.E. Roelfstra, T. A. M. S. a. J. E. K. (1994). Defining and application of stress-analysisbased temperature difference limits to prevent previous termearly-agenext term
cracking in concrete structures. Proceedings #25 of the International RILEM
Symposium: Thermal Cracking in Concrete at previous termEarly Ages.
Princigallo, A., P. Lura, et al. (2003). "Early development of properties in a cement paste:
A numerical and experimental study." Cement and Concrete Research 33(7):
1013-1020.
Qing, Y., Z. Zenan, et al. (2007). "Influence of nano-SiO2 addition on properties of
hardened cement paste as compared with silica fume." Construction and Building
Materials 21(3): 539-545.
Sanchez, F. and C. Ince (2009). "Microstructure and macroscopic properties of hybrid
carbon nanofiber/silica fume cement composites." Composites Science and
Technology 69(7-8): 1310-1318.
180
Senff, L., J. A. Labrincha, et al. (2009). "Effect of nano-silica on rheology and fresh
properties of cement pastes and mortars." Construction and Building Materials
23(7): 2487-2491.
Shui, Z.-h., R. Zhang, et al. "Effects of mineral admixtures on the thermal expansion
properties of hardened cement paste." Construction and Building Materials 24(9):
1761-1767.
Singh, N. B. and B. Middendorf (2007). "Calcium sulphate hemihydrate hydration
leading to gypsum crystallization." Progress in Crystal Growth and
Characterization of Materials 53(1): 57-77.
Wikipedia (2009). Concrete, Wikipedia.
Yu, X. and V. P. Drnevich (2004). Soil Water Content and Dry Density by Time Domain
Reflectometry, ASCE. 130: 922-934.
Yu, X., Drnevich, VP (2005). "Density Compensation of TDR Calibration for
Geotechnical Applications." Journal of ASTM International 2(1): 16.
181