Characterization of Pore Structure in Enhanced
Porosity Concrete
Kathleen Lowa, Narayanan Neithalathb
a
Class of 2008, Department of Civil & Environmental Engineering,
Environmental REU
b
Narayanan Neithalath, Assistant Professor,Clarkson University, Potsdam, NY,
13699-5710
Introduction
Enhanced porosity concrete (EPC) is proportioned using little or no fine aggregates (sand) so as
to create a network of interconnected pores. EPC potentially contributes to environmental sustainability
because of its capability to drain surface water quickly through its pore network, thereby making it a
useful stormwater management tool. Other possible benefits of EPC include filtering automobile
pollutants from entering waterways and reducing the tire-pavement interaction noise in roadways [1].
The large sized voids (2-5 mm, compared to the micrometer range pores in normal concrete), and
its random distribution makes the characterization of EPC a non-trivial issue. Also, pore size and porosity
alone cannot be used to characterize the functional effectiveness of EPC. Other pore structure features
that account for pore space distribution and connectivity are needed. In this project, an attempt is made to
characterize the pore structure of EPC proportioned using two different aggregate sizes, aggregate-cement
ratios, and water-cement ratios.
Sand was added as a part of the coarse aggregate in half of the
specimens. One half of the total number of specimens was hand-compacted while the other half was
vibrated at 300 rpm for 2 minutes.
Experiments were factorially designed to produce 16 sets of
specimens, on which the performance evaluation and pore structure characterization were carried out.
The functional characteristic of the system was represented using hydraulic conductivity while
image analysis and statistical tools were used to describe the porosity, pore sizes, and their variations.
Electrical impedance spectroscopy was used to determine the degree of interconnectedness of the pore
system in EPC. Weibull distribution was used to describe the data acquired from image analysis on pore
area and pore sizes because of its ability to scale and shape the distribution in accordance with the wide
range of data.
Determining Hydraulic Conductivity
A falling head permeability cell (Figure 1) was used to measure the saturated hydraulic conductivity of
EPC specimens. The permeability cell consists of two acrylic tubes, a valve, and a drainpipe. The EPC
specimen was enclosed in a latex sleeve, and was fully saturated with water. The time taken for the water
level to drop from an initial level of h2 to a final level of h1 in the top acrylic tube was recorded for the
calculation of the hydraulic conductivity (Eqn. 1), which was derived from Darcy’s law:
(Eqn. 1)
K is the permeability constant, A1 and A2 are the cross-sectional area of the sample and tube, and l is the
length of the specimen [2]. The hydraulic conductivities of
the EPC specimens ranged from 0.001 to 0.008 m/s in this
study.
Measuring Porosity:
The specimens which were enclosed in the latex
sleeves were sealed with silicone sealant at one end, and
attached to metal plates for the determination of porosity.
Water was added to fill the pores of the specimens. The
mass of water needed to fill the pores of the specimen was
converted to volume to give the overall porosity.
Determining Pore Connectivity:
Figure 1: Falling Head Permeability Cell
A
Solartron
1260™
Impedance/Gain-Phase
analyzer was used to carry out the Electrical Impedance Spectroscopy (EIS) measurements in order to
determine the pore connectivity. The specimen was filled with an electrolyte solution (3% NaCl having a
conductivity of 4.4 S/m) and placed between two metal plates prior to being connected to the impedance
analyzer. Through sweeping a range of frequencies from 1Hz to 1MHz, a Nyquist plot of
imaginary versus real impedance was produced. The bulk resistance (Rb) corresponds to the real
impedance when the imaginary impedance is minimized. The Rb facilitates calculation of the
effective sample conductivity. Using this value, the known electrolyte conductivity (σp) and the
specimen porosity (φp) was used to determine the pore connectivity factor (βp) using the equation
shown below (Eqn. 2) below [3].
σ eff= σp φp βp
(Eqn. 2)
Image Acquisition
The cylindrical specimens (95 mm diameter x 150 mm height) were sawed into three sections
each for image analysis. To create greater contrast of the images, the faces of the specimens were
darkened by a fat-tip marker and a pasty solution of white glass powder used to fill the pores of the
specimen [4]. Scanned images were processed using an image processing software (Image J™) by the use
of threshold, hole fill, open, and close operations. Other image processing functions included despeckle
and median, min, and max filters [5]. Figures 2 and 3 depict a typical grayscale image and a processed
image after the operations described earlier.
Figure 2: Initial Grayscale image
Figure 3: Processed Image in Image J
Data Analysis
Weibull distribution was used to model the wide range of data on pore areas obtained from
image analysis. Defined by its shape and scale parameters, β and α respectively, the Weibull distribution
creates a distribution based
Predicted and Actual Area Probabilities
0.9
on relativity in the set of data
0.8
[6,7]. A probability plot of
0.7
Predicted
R(t)
0.6
the complete data is used to
Actual
0.5
0.4
determine
0.3
parameters. The actual data
the
Weibull
0.2
plotted
0.1
0
along
with
the
cumulative distribution
0
10
20
30
Pore Area (mm^2)
Figure 5: Predicted and Actual Area Probabilities
40
function (cdf) shows that the
Weibull distribution is an
accurate model for data analysis. Figure 5 shows the cdf and actual data of pore areas for an 18.2%
porosity specimen. Comparing the parameters of different specimens, it is found that as the porosity
increases, the β values for the pore areas decreases, and the parameter α increases with increasing pore
areas. Since the pore areas increase with increasing porosity, it can be deduced that α increases with
increasing porosity. The decreasing β values create cdf’s shifted in the downward direction, and
increasing α indicate a flattening of the curve [8]. Both of these factors imply that there is more
probability of finding fewer larger pore areas than it is to find a larger range of smaller pore areas as
porosity is increased. This is also shown by the linear correlation between α and average pore areas.
Conclusion
This research study combines the commonly used methods of performance estimation of EPC
with statistical analysis of pore structure in order to develop a fundamental understanding of the influence
of pore structure characteristics on performance of this material. The image analysis procedure and the
Weibull parameters of the pore areas provide significant details about the pore geometry and its
distribution. This study has dealt with a limited number of EPC specimens, but it is anticipated that the
methodologies developed could be adopted to a larger sample size, thereby resulting in material models
that relate to the performance.
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