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. 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