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Determination of air-void parameters of hardened cement-based materials using X-ray computed tomography
Construction and Building Materials 37 (2012) 93–101
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Determination of air-void parameters of hardened cement-based materials using X-ray computed tomography Kwang Yeom Kim a, Tae Sup Yun b,⇑, Jinhyun Choo c,1, Dong Hun Kang b, Hyu Soung Shin a a
Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang 411-712, Republic of Korea School of Civil and Environmental Engineering, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 120-749, Republic of Korea c Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA b
h i g h l i g h t s " The 3D X-ray CT imaging provides efficient and reliable estimation of air-voids parameters for cement-based materials. " The spacing factor in 3D space is applicable to the quantification of heterogeneous distribution of air-voids. " The representativeness of parameters increases by minimizing sampling effects.
a r t i c l e
i n f o
Article history: Received 14 June 2012 Received in revised form 29 June 2012 Accepted 13 July 2012 Available online 24 August 2012 Keywords: X-ray CT Cement Air-void parameters Air content Spacing factor
a b s t r a c t This paper presents an attempt to tackle limitations in the two-dimensional (2D) stereological characterization of the air-void parameters of hardened cement-based materials by employing three-dimensional (3D) X-ray computed tomography (CT), a technique capable of simultaneously imaging numerous sections within a specimen. Using three hardened cement paste specimens composed of different air-void systems, we performed sensitivity analyses in terms of the number of traverse lines employed for a single section and the number of sampling sections across the height of a specimen. Parameters for a single section converged rapidly as the number of traverse lines increased, although unacceptable variations were in evidence across multiple sections. When the number of sampling sections exceeded about 10, a set of representative air-void parameters was successfully obtained within a standard variation of less than 10% of average values. The spacing factor and air content measures obtained via CT image analysis were in good agreement with previously reported data and with the original spacing factors defined for 3D space. Some advantages found in the use of X-ray CT imaging for determining air-void parameters are discussed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction The determination of the air-void parameters of cement-based materials is essential in assessing the freeze–thaw durability of those materials [1]. Quantitative air-void characterization has generally been carried out via stereological examination of twodimensional (2D) surface sections to gain an understanding of the three-dimensional (3D) features of a material, a useful technique when there is a lack of 3D information. In practice, a set of air-void parameters (typically the air content and the spacing factor, which in fact are 3D quantities) are determined via stereological examination of a 2D polished section using an optical
⇑ Corresponding author. Tel.: +82 221235805; fax: +82 23645300. E-mail addresses: [email protected] (K.Y. Kim), [email protected] (T.S. Yun), [email protected] (J. Choo), [email protected] (D.H. Kang), hyushin@ kict.re.kr (H.S. Shin). 1 Formerly at: Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang 411–712, Republic of Korea. 0950-0618/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.conbuildmat.2012.07.012
microscope, by means of the linear-traverse method or the pointcount method described in ASTM C457 [2]. Even for a single section, these conventional methods demand highly time-consuming and tedious work (approximately 3 h to obtain the parameters of a single polished section); hence, several alternative methods have been proposed to overcome the shortcomings of these conventional procedures, whether by employing different stereological methods [3,4] or by using other imaging devices [5–7]. Although these methods have enabled more efficient and objective determination of parameters, they still require a great deal of effort for sample preparation so as to examine a number of sections. Since the spatial distribution of air-voids is heterogeneous, however, parameters obtained from single sections may be sensitive to the number and location of the sections sampled. X-ray computed tomography (CT) imaging is a nondestructive method for obtaining a large number of consecutive sectional images of the internal microstructure of specimens of interest. It has been used in several studies to characterize the engineering properties of cement-based materials in terms of such parameters
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 1. 3D X-ray CT images of the air-void systems of the tested specimens. Each specimen has a diameter of 10.5 mm and a height of 8.9 mm after cropping from original reconstructed images in order to eliminate the noise and irregularity of the boundary surface.
as air-void space [8], spatial distribution of air content under axial loading [9], and clogging [10]. In addition to these applications, it is feasible to utilize X-ray CT 2D sectional images to determine the air-void parameters of cement-based materials, including by means of the linear-traverse method, as an alternative to conventional stereological methods. If this concept is viable, it offers the possibility of using X-ray CT imaging to estimate air-void parameters and their spatial distributions while avoiding the excessive time and costs in preparing specimens and processing data. It is also expected that the effects of heterogeneity of air-void distributions can be reduced by increasing representativeness. This paper presents an attempt to determine the air-void parameters of cement-based materials by implementing the methods designated in ASTM C457 on a large number set of X-ray CT sectional images. Specifically, an experimental investigation of sensitivities to sampling conditions was conducted for three hardened cement paste specimens prepared to exhibit a range of air content, controlled by the amount of air-entraining admixtures (AEAs) used. Prior to conducting the linear-traverse method on the X-ray CT images of the specimens, a series of image processing and treatment procedures were applied to the images. Thereafter, sensitivity analyses were undertaken for the sampling conditions in terms of the number of traverse lines on a single section and the number of sections taken from a single specimen. The validity of the parameters obtained via the proposed processes was assessed by comparison with published data and with the original spacing factors in 3D space proposed by Powers [11].
Fig. 2. 2D sliced image. Dark spots indicate air-voids and bright color denotes cement matrix. spatial resolution of up to 6.18 lm, and its maximum voltage and current are 225 kV and 3.0 mA, respectively, providing sufficient penetrating ability. The CT images used in this study were obtained at 150 kV and 100 lA. A CCD camera was used as a flat-panel detector to collect X-ray attenuation information after the radiation had passed through the specimen. The detector measures at 409.6 409.6 mm with a pixel pitch of 200 lm and 2.5 lp/mm (line pairs per millimeter) of limiting resolution. The maximum wobbling allowance of the manipulator, which determines the scanning location of the rotating specimen, was 5 lm. This value lies within the range of correction ability during the reconstruction process. Each image gathered in this study had a pixel size of 0.0108 0.0108 mm with 1024 1024 pixels. A total of 1024 images were taken at intervals of 0.0087 mm.
2. Experimentation 2.1. Materials and sample preparation Three specimens were prepared by adding varying amounts of AEAs to cement mixtures to control air-void entrainment. The mix ratio of cement, sand and water for the three specimens was the same, at 2:5:1. The amount of AEAs (sodium lauryl ether sulfate, SLES) was 0%, 3%, and 8% of cement weight for the Non-AE, AE-1 and AE-2 specimens, respectively. Specimens were carefully mixed for 5 min (1 min before adding water and 4 min after adding water) to facilitate the reaction. The maximum specimen size for the X-ray CT device is limited because the required size of a representative specimen depends on the size of the aggregate involved. Therefore, in preparing the specimens, only fine aggregates were used [2]. Compared with the irregular characteristics (in terms of distribution, shape, and size) of entrapped air in the Non-AE specimen hardened without AEAs, more uniform air-void systems were expected in the AE specimens. Each specimen was cast in a cylindrical plastic container of 100 mm diameter and 200 mm height for 7 days following 24 h of moist conditioning. To compare the air-void parameters obtained via the conventional and proposed methods, the linear-traverse method was conducted for the original specimens using an optical microscope, as described in ASTM C457. Thereafter, the specimens were cut to a cylinder 12 mm in diameter and 10 mm in height for X-ray CT imaging. 2.2. Acquisition of CT images The CT equipment used in this study was the X-EYE CT System (SEC Corporation, Korea). The microfocus X-ray tube in this system is capable of attaining a high
2.3. Enhancement of CT images Fig. 1 provides a 3D visualization of the qualitative differences in the air-void systems among the specimens by stacking the raw CT images. Note that the marginal boundary volume of the specimens is cut slightly in the images so that the size of the 3D stacked image is smaller than that of the cored cylindrical specimens. Xray CT images commonly contain certain inherent artifacts, namely cupping and rings, that stem from beam-hardening and data inconsistencies in image reconstruction [12,13]. Therefore, prior to extracting quantitative information from the X-ray CT images, it was necessary to apply a series of image processing and treatment techniques to the raw CT images. Differences in the absorption of X-ray energy passing through a specimen result in beam-hardening, which manifests in ‘‘cupping,’’ wherein the periphery of a specimen appears light while the center is markedly darker. In addition, X-ray beam instability and defective detector elements (e.g., dead pixels in a CCD) often cause periodic ring-shaped artifacts emanating from the image center corresponding to the rotational axis of the CT device. While careful hardware calibration (e.g. geometric calibration and beam-shaping filtering) can improve noise homogeneity in the projection image and in turn reduce beam-hardening, the radially spaced ring artifacts persist in the raw images. These ring artifacts can be satisfactorily removed via a series of image processing techniques. As shown in Fig. 2, the CT image can be expressed as P(x,y) in the Cartesian coordinate system, where dark spots in the image indicate air-voids and bright regions denote the cement matrix. To reduce ring artifacts, a coordinate transformation and Fourier transformation were conducted as shown in Fig. 3a: P(x,y) was transformed to polar coordinate P(r,h) where the horizontal strips exist mainly at low values of r, originating from ring artifacts in a Cartesian space. The
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(a)
θ
r
(b)
(c)
Fig. 3. Image processing procedure: (a) CT image transformed to the polar coordinate plotted in Cartesian space; (b) CT image after removal of horizontal stripes; (c) CT image after noise reduction in Cartesian coordinate. Arrows and circle denote the same indicative air-voids in both polar and Cartesian space. Fourier transformation placed these stripes on the high-frequency end of the spectrum, allowing them to be removed via low-pass filtering [14]. A low-pass notch filter was applied to bypass all frequencies except those in the designated frequency band. The sharp-edged off–on–off notch filter generally magnifies discontinuity; thus, a Gaussian filter was applied to the filter to smooth the edge. The results of the inverse Fourier transformation, by which the unfavorable horizontal strips were effectively removed without destroying the original air-voids, are presented in Fig. 3b. The images were finally transformed to Cartesian space (Fig. 3c). The same indicative air-voids in both Cartesian and polar space are denoted by arrows and a circle in Fig. 3b and c. Note that after noise reduction the image size was 9.47 mm 9.47 mm (877 877 pixels). Segmentation of the air-voids and cement-matrix in the CT images was conducted via thresholding (e.g., a binary conversion of the images with respect to a threshold value). The distribution of the pixel values is plotted in Fig. 4a, where two unique pixel groups exist, based on the higher pixel values of the cement matrix as compared to the air-voids. Once the threshold value T is determined, pixel values greater than T assume a zero value, while those smaller than T become unity (e.g., 1). Thus, the selection of the threshold value T critically affects the quantification of the air-voids and corresponding air-void parameters [15]. The most common and readily applicable method is to minimize intra-class variance via Otsu’s method [16], as employed in a vast number of image processing studies. This method iteratively computes the class probability of two arbitrarily divided classes via a threshold value, and determines T when intra-class variance is minimized. This process is iteratively applied to the entirety of the 2D images to extract air-voids. The binary image finally obtained is shown in Fig. 4b.
3. Determination of air-void parameters from CT images The determination of air-void parameters from X-ray CT images is executed via a series of procedures. First, the region of interest
(ROI) is defined by cropping the circular binary image (Fig. 4b) into a square image of pixel size B B (491 491 pixels is equivalent to 5.3 mm 5.3 mm). Then, the linear-traverse method designated in ASTM C457 [2] is undertaken to estimate the air-void parameters via the following steps (see the schematic illustrated in Fig. 5): Step 1: Select the number of traverse lines (i) equivalently spaced on each section image of size B B. Step 2: Select the number of section images (j) among the total number of square section images available (K). Step 3: Calculate the total number of air-voids (N) intersecting all the selected traverse lines of a specimen and the total number of air-void pixels (S) for each line. Then, the total traversed length (Tt) for each section image becomes B i dx where dx is the pixel size, which varies depending on the imaging conditions of the CT device. The length traversed through the air-voids (Ta) on a selected section image is equal to S dx. The air content, void frequency, specific surface, and spacing factor are then calculated via the equations summarized in Table 1. The minimum lengths of the accumulated traverse lines for the linear-traverse method given in ASTM C457 are specified with respect to the aggregate sizes of target materials (e.g. 1397 mm for a specimen with 4.75 mm aggregates). Therefore, at least 264 lines (1400 mm) should be selected and examined to satisfy this requirement in the case of a 4.75 mm aggregate. It is obvious that
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(a)
(b) Threshold value T
Air-voids
Solid
Fig. 4. (a) Histogram of pixel values. (b) Binary image (white color denotes the identified voids).
the set of air-void parameters should be determined at numbers i and j, where these are large enough for the standard deviation of the parameters to fall within an acceptable tolerance (e.g. 10% of the average values of the parameters used in this study). 4. Results and discussion The air-void parameters obtained via the proposed CT-based method were validated via two steps. First, based on the sensitivity analyses in terms of the numbers of traverse lines and sectional images from a given specimen, representative values of the air content and spacing factors were determined. Thereafter, the values obtained were compared with previously reported data and with the original spacing factor in 3D space proposed by Powers [11]. 4.1. Number of traverse lines per sectional image The sensitivity of parameters to the number of traverse lines was first examined by computing the air content and spacing factors based on 5 and 491 (maximum) traverse lines being drawn for each image. The distributions of both parameters obtained from 1024 images are plotted in Fig. 6. It is clearly shown that the formation of entrained-air via AEAs resulted in higher air content and a lower spacing factor with smaller standard deviations. The higher variation in the parameters when using five traverse lines suggests the necessity of employing a sufficient number of traverse lines for analysis. To determine how many lines are sufficient for a single sectional image, the variations were calculated in the means and standard deviations of the parameters as the number of traverse lines i increased from 5 to 491, as shown in Fig. 7. In other words, statistical values represented by histograms, as shown in Fig. 6, for a given number of traverse lines were computed from 1024 images for varying numbers of traverse lines i. The means and standard deviations, as shown in Fig. 7, exhibit a converging trend even before i reaches the minimum requirement for traversed length (264 lines) given by ASTM C457 [2]. The deviation of parameters in the AE-2 specimen was smaller than those of other specimens, owing to the more uniform air-void distributions achieved by air-entrainment. 4.2. Number of sectional images per specimen As demonstrated above, the air-void parameters determined from a single sectional image do not appear to exhibit representativeness. Thus, it was investigated whether, when using a sufficient
Select i lines from each square image whose size is B×B
Select j images among K images Fig. 5. Schematic diagram for selection of traverse lines and sectional images from a 3D CT image.
number of traverse lines i (= 491), evolutions of parameters could be established by increasing the selected number of section images j. From the 1024 sectional images available for each specimen, j images were randomly selected separately, and then the averages of the parameters from the selected j images were calculated. This process was performed 1024 times for each random selection case. All cases of the selection of j images were considered, whereupon the number of average air-void parameters corresponded to C(1024,j) where C denotes the combination. The evolutions of mean, maximum, and minimum of the averaged parameters of j images are shown in Fig. 8 as j increases from 1 to 1024. The range of the parameters fluctuates when j is low, but the range becomes noticeably smaller as j increases. More importantly, the mean of the parameters (solid symbol) remains quasiconstant when j becomes larger than 10, and virtually converges to the values averaged from all 1024 sections, thus corresponding to the entire 2D image set. This convergence indicates that the parameters obtained by averaging the values of the 1024 sections may be representative for a given specimen. Subsequently, the air-void parameters averaged from a complete set of CT images (i.e. 1024 sectional images) were compared to those obtained from a single instance of 2D sectional data. Table 2 summarizes two sets of air-void parameters from the same specimen, one estimated via X-ray CT imagery and another (values in parenthesis) estimated via a polished section viewed through an optical microscope. It was observed that the parameters obtained from a single polished section differed somewhat from the averaged values derived from the CT image analysis. However, it is notable that the values obtained from the single section lie within the range of possible variations of the parameters shown in Fig. 8. This observation implies that the discrepancy in values may be
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Table 1 Summary of air-void parameter equations from ASTM C457. Parameters Equations
Air content (%) A = (Ta/Tt) 100
Void frequency (mm n = N/Tt
1
)
Specific surface (mm–1)
Spacing factor (mm)
a = 4N/Ta
L = 3/a[1.4(l + Tp/Ta)1/3–1] when P/A > 4.342 L = Tp/4N when P/A < 4.342
(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 6. Distributions of air content and spacing factor of three specimens.
attributed to the heterogeneity of the air-void distribution in the specimens, thus substantiating our postulation that the parameters determined from a single section may be sensitive to the number of specimens and the location of specimens within the section. In addition, as shown in Table 2, the determination of air content from the single polished section failed to capture the higher airvoids entrained in the AE-2 specimens, presumably due to the heterogeneous distribution of air-voids. 4.3. Validation of the parameters This section evaluates the validity of the air-void parameters obtained through X-ray CT imaging. The air content and the spac-
ing factors have an inversely proportional relationship with each other [6]. Although the relationship is not strongly correlated, it can serve as a useful guide for validating the values of the parameters obtained in this study. The relationship between air content and spacing factors derived from this study (solid symbols) was superimposed on the data from the literature (open symbols) [6,17,18], and, as is clearly shown in Fig. 9, the obtained values exhibit good quantitative agreement with previously reported data. The characterization of 3D air-void configuration by reconstructing sliced images allows for deriving the spacing factor in 3D space as originally suggested by Powers [11]. Indeed, the equations suggested in ASTM C457 (Table 1) were devised based on the principle of stereology that aims for the estimation of 3D
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 7. Variations in the mean and one standard deviation of air content and spacing factor according to the number of traverse lines i, for the three specimens.
characteristics based on a 2D examination when 3D information is not available. However, when 3D information is available, it is better to calculate the air-void parameters rather than estimate them via any hypothesis or employing any principles. The spacing factor was originally suggested as half the distance between two diagonally adjacent voids in a cubic assembly, assuming that all voids are mono-sized spheres equally spaced throughout the entire volume [11]. Obviously, this distance cannot be measured by conventional optical methods; hence the stereology-based linear-traverse method or modified count methods have been widely used to estimate the spacing factor. Unlike optical devices, however, X-ray CT enables air-voids to be characterized and then the spacing factor to be derived directly in 3D space. To calculate the spacing factor in 3D, the spatial configuration of the air-voids was quantified by stacking the binary air-void images, as shown in Fig. 10. It may be seen that the air-voids are randomly
distributed without spatial bias, and that the number of air-voids increases markedly by air entrainment. The air-void objects, each of which was composed by combining the interconnected air-void voxels, were quantitatively defined and the number of voxels for each air-void object was obtained. The total number of air-voids, mean volume, and equivalent diameter of the air-voids for each specimen are shown in Fig. 10. The distribution curves of the equivalent diameters of air-voids were well-fitted to the log-normal distribution. As expected, the total number of air-voids increased with the amount of AEAs. The 3D configuration of air-voids can be idealized by conceptualizing them as mono-dispersed spheres whose number and total volume are equivalent to the original air-voids randomly distributed in the specimens. To determine the spacing factor in 3D, spheres were equally spaced in a cubic volume, as illustrated in Fig. 11. The spacing factor can then be determined based on ideally
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 8. Evolution of average air content and spacing factor with an increasing number of selected images j. The error bar denotes one standard deviation.
Table 2 Estimated air-void parameters. Specimen Non-AE AE-1 AE-2
VOI size (mm3)
Air content (%)
Void frequency (mm
255.353
3.780 (6.3) 6.902 (6.8) 8.271 (6.7)
0.128 (0.156) 0.326 (0.223) 0.508 (0.270)
1
)
Spacing factor (mm)
Specific surface (mm
0.535 (0.403) 0.263 (0.291) 0.190 (0.240)
16.367 (9.8) 21.152 (13.1) 25.113 (16.1)
1
)
Note: Values in parentheses are those estimated from optical-microscopic examination of a polished section.
re-distributed spheres by considering the definition of the spacing factor. Therefore, results for the spacing factors from the original definition derived from the 2D stereological method using a sufficient number of 2D section images were compared with the values from the direct 3D method described herein. It can be postulated that the conventional 2D stereological method is acceptable for estimating representative spacing factors, provided that a sufficient number of traverse lines and 2D sampling sections are used.
4.4. Discussion The capability of X-ray CT to quantify the distributions of airvoid parameters has a few important implications. First, it is important that X-ray CT can successfully capture the inherent heterogeneity of the air-void distribution with no further effort. Furthermore, variations in the parameters can be an effective means of verifying the consistency of the parameters. As a criterion for
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Fig. 9. Estimated air-void parameters in this study superimposed on previously reported data. Fig. 11. Comparison between spacing factors estimated by averaging the values from 2D cross-sections following ASTM C457 and obtained by calculating the distances between equivalent spherical voids rearranged in 3D space as suggested by Powers (1954).
(a) Non-AE
(a) AE-1
(b) AE-2
Fig. 10. 3D configuration of air-voids and the distribution of equivalent air-void diameters (5.3 mm 5.3 mm 8.9 mm).
K.Y. Kim et al. / Construction and Building Materials 37 (2012) 93–101
freeze–thaw durability, air content is often used instead of the spacing factor for the sake of convenience. Revisiting Fig. 8, one may observe a higher variation in air content than in the spacing factor estimated from the 2D sectional images. This observation indicates that the determination of air content from a few crosssectional images can be misleading. This finding agrees well with the experience of experts that many concrete specimens exhibit desirable air content even when their spacing factors exceed specified limits [6]. This study substantiates the argument that the spacing factor is a more reliable measure for evaluating the quality of the dissemination of air-voids in cement-based materials. In this regard, the quantification of air-void parameters using X-ray CT imaging is a promising option for improving the current system of air-void assessment. 5. Conclusions X-ray CT is an efficient tool in the nondestructive high-resolution characterization of the microstructural configuration of materials. This paper has described the application of X-ray CT imaging in determining the air-void parameters of cement-based materials. As opposed to the conventional method of viewing a 2D section through an optical microscope, the standardized stereological method (the linear-traverse method) was conducted on a large number set of CT images of three cement paste specimens prepared with different air-void content as generated by air entrainment. Then, the linear-traverse method was implemented for sets of sectional CT images of the prepared specimens. The results indicate that due to the inherent heterogeneity of air-void distribution, the parameters estimated are significantly affected by the number of traverse lines selected for a single section and the number of sectional images selected across the volume of specimens. However, it is also shown that using a sufficient number of traverse lines and sectional images allows the derivation of values that can be regarded as representative and reliable, based on comparisons with published data and with a spacing factor calculated based on the original definition of the spacing factor in 3D space. The analysis of sets of X-ray CT images was also able to capture quantitative variations in the air-void parameters as controlled by air entrainment. The advantages offered by the X-ray CT imaging method can be summarized as follows. First, the method requires no preliminary physical treatment of specimens, such as cutting and polishing. Second, the method can increase the representativeness of the parameters by minimizing sampling effects. Finally, the method enables quantification of the heterogeneity of air-void distribution. In consideration of the limitations of the CT device available for this study, the specimen sizes (and thus the applicable aggregates) for X-ray CT imaging were intentionally restricted to obtain high resolution imaging of air-void distribution. However, since the size requirements for representative specimens are dependent on the size of the aggregate involved, it should be noted that it is necessary to prepare the size of specimens in consideration of the size of the aggregates used. With this provision taken as uncontroversial, we believe the proposed X-ray CT based method to be valid. Admittedly, the practical size of a cored specimen (>10 cm) for a
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gravel aggregate needs to be taken into consideration in making the proposed method practically available, a matter which will be investigated and reported shortly. Further, it is anticipated that improvements in the resolution capabilities of X-ray CT images will provide additional intriguing possibilities for the enhancement of conventional methods of determining the air-void parameters of cement-based materials. Acknowledgements This work was supported by the New & Renewable Energy program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean Ministry of Knowledge Economy (No. 2010T100200494) and the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Nos. 2011-0022883, 2011-0005593). References [1] Federal Highway Administration (FHWA). Freeze–thaw resistance of concrete with marginal air content; 2006. [2] American Society of Testing and Materials (ASTM). ASTM C457: standard test method for microscopical determination of parameters of the air-void system in hardened. Concrete; 2010. [3] Aligizaki KK, Cady PD. Air content and size distribution of air voids in hardened cement pastes using the section-analysis method. Cem Concr Res 1999;29(2): 273–80. [4] Jakobsen UH, Pade C, Thaulow N, Brown D, Sahu S, Magnusson O, et al. Automated air void analysis of hardened concrete – a round robin study. Cem Concr Res 2006;36(8):1444–52. [5] Corr DJ, Juenger MCG, Monteiro PJM, Bastacky J. Investigating entrained air voids and Portland cement hydration with low-temperature scanning electron microscopy. Cem Concr Compos 2004;26(8):1007–12. [6] Pleau R, Pigeon M, Laurencot J-L. Some findings on the usefulness of image analysis for determining the characteristics of the air-void system on hardened concrete. Cem Concr Compos 2001;23(2–3):237–46. [7] Zalocha D, Kasperkiewicz J. Estimation of the structure of air entrained concrete using a flatbed scanner. Cem Concr Res 2005;35(10):2041–6. [8] Promentilla MAB, Sugiyama T, Shimura K. Three dimensional characterization of air void system in cement-based materials. In: 3rd Asian concrete federation (ACF) international conference, Ho Chi Minh, Vietnam; 2008. p. 940–7. [9] Wong RCK, Chau KT. Estimation of air void and aggregate spatial distributions in concrete under uniaxial compression using computer tomography scanning. Cem Concr Res 2005;35(8):1566–76. [10] Manahiloh KN, Muhunthan B, Kayhanian M, Gebremariam SY. X-ray computed tomography and nondestructive evaluation of clogging in porous concrete field samples. J Mater Civ Eng 2012;24(8):1103–9. [11] Powers TC. The air requirement of frost-resistant concrete. Proc Highway Res Board 1949;29:184–211. [12] Hiriyannaiah HP. X-ray computed tomography for medical imaging. Signal Process Mag, IEEE 1997;14(2):42–59. [13] Bernard D, Chirazi A. Numerically enhanced microtomographic imaging method using a novel ring artifact filter. In: Desrues J, Viggiani G, Besuelle P, editors. Advances in X-ray tomography for geomaterials; 2006. [14] Raven C. Numerical removal of ring artifacts in microtomography. Rev Sci Instrum 1998;69(8):2978–80. [15] Peterson K, Carlson J, Sutter L, Van Dam T. Methods for threshold optimization for images collected from contrast enhanced concrete surfaces for air-void system characterization. Mater Charact 2009;60(7):710–5. [16] Otsu N. A threshold selection method from gray-level histograms. Syst, Man Cyber, IEEE Trans 1979;9(1):62–6. [17] Pigeon M, Gagné R, Foy C. Critical air-void spacing factors for low water– cement ratio concretes with and without condensed silica fume. Cem Concr Res 1987;17(6):896–906. [18] Plante P, Pigeon M, Foy C. The influence of water-reducers on the production and stability of the air void system in concrete. Cem Concr Res 1989;19(4): 621–33.
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Determination of air-void parameters of hardened cement-based materials using X-ray computed tomography Kwang Yeom Kim a, Tae Sup Yun b,⇑, Jinhyun Choo c,1, Dong Hun Kang b, Hyu Soung Shin a a
Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang 411-712, Republic of Korea School of Civil and Environmental Engineering, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 120-749, Republic of Korea c Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA b
h i g h l i g h t s " The 3D X-ray CT imaging provides efficient and reliable estimation of air-voids parameters for cement-based materials. " The spacing factor in 3D space is applicable to the quantification of heterogeneous distribution of air-voids. " The representativeness of parameters increases by minimizing sampling effects.
a r t i c l e
i n f o
Article history: Received 14 June 2012 Received in revised form 29 June 2012 Accepted 13 July 2012 Available online 24 August 2012 Keywords: X-ray CT Cement Air-void parameters Air content Spacing factor
a b s t r a c t This paper presents an attempt to tackle limitations in the two-dimensional (2D) stereological characterization of the air-void parameters of hardened cement-based materials by employing three-dimensional (3D) X-ray computed tomography (CT), a technique capable of simultaneously imaging numerous sections within a specimen. Using three hardened cement paste specimens composed of different air-void systems, we performed sensitivity analyses in terms of the number of traverse lines employed for a single section and the number of sampling sections across the height of a specimen. Parameters for a single section converged rapidly as the number of traverse lines increased, although unacceptable variations were in evidence across multiple sections. When the number of sampling sections exceeded about 10, a set of representative air-void parameters was successfully obtained within a standard variation of less than 10% of average values. The spacing factor and air content measures obtained via CT image analysis were in good agreement with previously reported data and with the original spacing factors defined for 3D space. Some advantages found in the use of X-ray CT imaging for determining air-void parameters are discussed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction The determination of the air-void parameters of cement-based materials is essential in assessing the freeze–thaw durability of those materials [1]. Quantitative air-void characterization has generally been carried out via stereological examination of twodimensional (2D) surface sections to gain an understanding of the three-dimensional (3D) features of a material, a useful technique when there is a lack of 3D information. In practice, a set of air-void parameters (typically the air content and the spacing factor, which in fact are 3D quantities) are determined via stereological examination of a 2D polished section using an optical
⇑ Corresponding author. Tel.: +82 221235805; fax: +82 23645300. E-mail addresses: [email protected] (K.Y. Kim), [email protected] (T.S. Yun), [email protected] (J. Choo), [email protected] (D.H. Kang), hyushin@ kict.re.kr (H.S. Shin). 1 Formerly at: Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang 411–712, Republic of Korea. 0950-0618/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.conbuildmat.2012.07.012
microscope, by means of the linear-traverse method or the pointcount method described in ASTM C457 [2]. Even for a single section, these conventional methods demand highly time-consuming and tedious work (approximately 3 h to obtain the parameters of a single polished section); hence, several alternative methods have been proposed to overcome the shortcomings of these conventional procedures, whether by employing different stereological methods [3,4] or by using other imaging devices [5–7]. Although these methods have enabled more efficient and objective determination of parameters, they still require a great deal of effort for sample preparation so as to examine a number of sections. Since the spatial distribution of air-voids is heterogeneous, however, parameters obtained from single sections may be sensitive to the number and location of the sections sampled. X-ray computed tomography (CT) imaging is a nondestructive method for obtaining a large number of consecutive sectional images of the internal microstructure of specimens of interest. It has been used in several studies to characterize the engineering properties of cement-based materials in terms of such parameters
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 1. 3D X-ray CT images of the air-void systems of the tested specimens. Each specimen has a diameter of 10.5 mm and a height of 8.9 mm after cropping from original reconstructed images in order to eliminate the noise and irregularity of the boundary surface.
as air-void space [8], spatial distribution of air content under axial loading [9], and clogging [10]. In addition to these applications, it is feasible to utilize X-ray CT 2D sectional images to determine the air-void parameters of cement-based materials, including by means of the linear-traverse method, as an alternative to conventional stereological methods. If this concept is viable, it offers the possibility of using X-ray CT imaging to estimate air-void parameters and their spatial distributions while avoiding the excessive time and costs in preparing specimens and processing data. It is also expected that the effects of heterogeneity of air-void distributions can be reduced by increasing representativeness. This paper presents an attempt to determine the air-void parameters of cement-based materials by implementing the methods designated in ASTM C457 on a large number set of X-ray CT sectional images. Specifically, an experimental investigation of sensitivities to sampling conditions was conducted for three hardened cement paste specimens prepared to exhibit a range of air content, controlled by the amount of air-entraining admixtures (AEAs) used. Prior to conducting the linear-traverse method on the X-ray CT images of the specimens, a series of image processing and treatment procedures were applied to the images. Thereafter, sensitivity analyses were undertaken for the sampling conditions in terms of the number of traverse lines on a single section and the number of sections taken from a single specimen. The validity of the parameters obtained via the proposed processes was assessed by comparison with published data and with the original spacing factors in 3D space proposed by Powers [11].
Fig. 2. 2D sliced image. Dark spots indicate air-voids and bright color denotes cement matrix. spatial resolution of up to 6.18 lm, and its maximum voltage and current are 225 kV and 3.0 mA, respectively, providing sufficient penetrating ability. The CT images used in this study were obtained at 150 kV and 100 lA. A CCD camera was used as a flat-panel detector to collect X-ray attenuation information after the radiation had passed through the specimen. The detector measures at 409.6 409.6 mm with a pixel pitch of 200 lm and 2.5 lp/mm (line pairs per millimeter) of limiting resolution. The maximum wobbling allowance of the manipulator, which determines the scanning location of the rotating specimen, was 5 lm. This value lies within the range of correction ability during the reconstruction process. Each image gathered in this study had a pixel size of 0.0108 0.0108 mm with 1024 1024 pixels. A total of 1024 images were taken at intervals of 0.0087 mm.
2. Experimentation 2.1. Materials and sample preparation Three specimens were prepared by adding varying amounts of AEAs to cement mixtures to control air-void entrainment. The mix ratio of cement, sand and water for the three specimens was the same, at 2:5:1. The amount of AEAs (sodium lauryl ether sulfate, SLES) was 0%, 3%, and 8% of cement weight for the Non-AE, AE-1 and AE-2 specimens, respectively. Specimens were carefully mixed for 5 min (1 min before adding water and 4 min after adding water) to facilitate the reaction. The maximum specimen size for the X-ray CT device is limited because the required size of a representative specimen depends on the size of the aggregate involved. Therefore, in preparing the specimens, only fine aggregates were used [2]. Compared with the irregular characteristics (in terms of distribution, shape, and size) of entrapped air in the Non-AE specimen hardened without AEAs, more uniform air-void systems were expected in the AE specimens. Each specimen was cast in a cylindrical plastic container of 100 mm diameter and 200 mm height for 7 days following 24 h of moist conditioning. To compare the air-void parameters obtained via the conventional and proposed methods, the linear-traverse method was conducted for the original specimens using an optical microscope, as described in ASTM C457. Thereafter, the specimens were cut to a cylinder 12 mm in diameter and 10 mm in height for X-ray CT imaging. 2.2. Acquisition of CT images The CT equipment used in this study was the X-EYE CT System (SEC Corporation, Korea). The microfocus X-ray tube in this system is capable of attaining a high
2.3. Enhancement of CT images Fig. 1 provides a 3D visualization of the qualitative differences in the air-void systems among the specimens by stacking the raw CT images. Note that the marginal boundary volume of the specimens is cut slightly in the images so that the size of the 3D stacked image is smaller than that of the cored cylindrical specimens. Xray CT images commonly contain certain inherent artifacts, namely cupping and rings, that stem from beam-hardening and data inconsistencies in image reconstruction [12,13]. Therefore, prior to extracting quantitative information from the X-ray CT images, it was necessary to apply a series of image processing and treatment techniques to the raw CT images. Differences in the absorption of X-ray energy passing through a specimen result in beam-hardening, which manifests in ‘‘cupping,’’ wherein the periphery of a specimen appears light while the center is markedly darker. In addition, X-ray beam instability and defective detector elements (e.g., dead pixels in a CCD) often cause periodic ring-shaped artifacts emanating from the image center corresponding to the rotational axis of the CT device. While careful hardware calibration (e.g. geometric calibration and beam-shaping filtering) can improve noise homogeneity in the projection image and in turn reduce beam-hardening, the radially spaced ring artifacts persist in the raw images. These ring artifacts can be satisfactorily removed via a series of image processing techniques. As shown in Fig. 2, the CT image can be expressed as P(x,y) in the Cartesian coordinate system, where dark spots in the image indicate air-voids and bright regions denote the cement matrix. To reduce ring artifacts, a coordinate transformation and Fourier transformation were conducted as shown in Fig. 3a: P(x,y) was transformed to polar coordinate P(r,h) where the horizontal strips exist mainly at low values of r, originating from ring artifacts in a Cartesian space. The
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(a)
θ
r
(b)
(c)
Fig. 3. Image processing procedure: (a) CT image transformed to the polar coordinate plotted in Cartesian space; (b) CT image after removal of horizontal stripes; (c) CT image after noise reduction in Cartesian coordinate. Arrows and circle denote the same indicative air-voids in both polar and Cartesian space. Fourier transformation placed these stripes on the high-frequency end of the spectrum, allowing them to be removed via low-pass filtering [14]. A low-pass notch filter was applied to bypass all frequencies except those in the designated frequency band. The sharp-edged off–on–off notch filter generally magnifies discontinuity; thus, a Gaussian filter was applied to the filter to smooth the edge. The results of the inverse Fourier transformation, by which the unfavorable horizontal strips were effectively removed without destroying the original air-voids, are presented in Fig. 3b. The images were finally transformed to Cartesian space (Fig. 3c). The same indicative air-voids in both Cartesian and polar space are denoted by arrows and a circle in Fig. 3b and c. Note that after noise reduction the image size was 9.47 mm 9.47 mm (877 877 pixels). Segmentation of the air-voids and cement-matrix in the CT images was conducted via thresholding (e.g., a binary conversion of the images with respect to a threshold value). The distribution of the pixel values is plotted in Fig. 4a, where two unique pixel groups exist, based on the higher pixel values of the cement matrix as compared to the air-voids. Once the threshold value T is determined, pixel values greater than T assume a zero value, while those smaller than T become unity (e.g., 1). Thus, the selection of the threshold value T critically affects the quantification of the air-voids and corresponding air-void parameters [15]. The most common and readily applicable method is to minimize intra-class variance via Otsu’s method [16], as employed in a vast number of image processing studies. This method iteratively computes the class probability of two arbitrarily divided classes via a threshold value, and determines T when intra-class variance is minimized. This process is iteratively applied to the entirety of the 2D images to extract air-voids. The binary image finally obtained is shown in Fig. 4b.
3. Determination of air-void parameters from CT images The determination of air-void parameters from X-ray CT images is executed via a series of procedures. First, the region of interest
(ROI) is defined by cropping the circular binary image (Fig. 4b) into a square image of pixel size B B (491 491 pixels is equivalent to 5.3 mm 5.3 mm). Then, the linear-traverse method designated in ASTM C457 [2] is undertaken to estimate the air-void parameters via the following steps (see the schematic illustrated in Fig. 5): Step 1: Select the number of traverse lines (i) equivalently spaced on each section image of size B B. Step 2: Select the number of section images (j) among the total number of square section images available (K). Step 3: Calculate the total number of air-voids (N) intersecting all the selected traverse lines of a specimen and the total number of air-void pixels (S) for each line. Then, the total traversed length (Tt) for each section image becomes B i dx where dx is the pixel size, which varies depending on the imaging conditions of the CT device. The length traversed through the air-voids (Ta) on a selected section image is equal to S dx. The air content, void frequency, specific surface, and spacing factor are then calculated via the equations summarized in Table 1. The minimum lengths of the accumulated traverse lines for the linear-traverse method given in ASTM C457 are specified with respect to the aggregate sizes of target materials (e.g. 1397 mm for a specimen with 4.75 mm aggregates). Therefore, at least 264 lines (1400 mm) should be selected and examined to satisfy this requirement in the case of a 4.75 mm aggregate. It is obvious that
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(a)
(b) Threshold value T
Air-voids
Solid
Fig. 4. (a) Histogram of pixel values. (b) Binary image (white color denotes the identified voids).
the set of air-void parameters should be determined at numbers i and j, where these are large enough for the standard deviation of the parameters to fall within an acceptable tolerance (e.g. 10% of the average values of the parameters used in this study). 4. Results and discussion The air-void parameters obtained via the proposed CT-based method were validated via two steps. First, based on the sensitivity analyses in terms of the numbers of traverse lines and sectional images from a given specimen, representative values of the air content and spacing factors were determined. Thereafter, the values obtained were compared with previously reported data and with the original spacing factor in 3D space proposed by Powers [11]. 4.1. Number of traverse lines per sectional image The sensitivity of parameters to the number of traverse lines was first examined by computing the air content and spacing factors based on 5 and 491 (maximum) traverse lines being drawn for each image. The distributions of both parameters obtained from 1024 images are plotted in Fig. 6. It is clearly shown that the formation of entrained-air via AEAs resulted in higher air content and a lower spacing factor with smaller standard deviations. The higher variation in the parameters when using five traverse lines suggests the necessity of employing a sufficient number of traverse lines for analysis. To determine how many lines are sufficient for a single sectional image, the variations were calculated in the means and standard deviations of the parameters as the number of traverse lines i increased from 5 to 491, as shown in Fig. 7. In other words, statistical values represented by histograms, as shown in Fig. 6, for a given number of traverse lines were computed from 1024 images for varying numbers of traverse lines i. The means and standard deviations, as shown in Fig. 7, exhibit a converging trend even before i reaches the minimum requirement for traversed length (264 lines) given by ASTM C457 [2]. The deviation of parameters in the AE-2 specimen was smaller than those of other specimens, owing to the more uniform air-void distributions achieved by air-entrainment. 4.2. Number of sectional images per specimen As demonstrated above, the air-void parameters determined from a single sectional image do not appear to exhibit representativeness. Thus, it was investigated whether, when using a sufficient
Select i lines from each square image whose size is B×B
Select j images among K images Fig. 5. Schematic diagram for selection of traverse lines and sectional images from a 3D CT image.
number of traverse lines i (= 491), evolutions of parameters could be established by increasing the selected number of section images j. From the 1024 sectional images available for each specimen, j images were randomly selected separately, and then the averages of the parameters from the selected j images were calculated. This process was performed 1024 times for each random selection case. All cases of the selection of j images were considered, whereupon the number of average air-void parameters corresponded to C(1024,j) where C denotes the combination. The evolutions of mean, maximum, and minimum of the averaged parameters of j images are shown in Fig. 8 as j increases from 1 to 1024. The range of the parameters fluctuates when j is low, but the range becomes noticeably smaller as j increases. More importantly, the mean of the parameters (solid symbol) remains quasiconstant when j becomes larger than 10, and virtually converges to the values averaged from all 1024 sections, thus corresponding to the entire 2D image set. This convergence indicates that the parameters obtained by averaging the values of the 1024 sections may be representative for a given specimen. Subsequently, the air-void parameters averaged from a complete set of CT images (i.e. 1024 sectional images) were compared to those obtained from a single instance of 2D sectional data. Table 2 summarizes two sets of air-void parameters from the same specimen, one estimated via X-ray CT imagery and another (values in parenthesis) estimated via a polished section viewed through an optical microscope. It was observed that the parameters obtained from a single polished section differed somewhat from the averaged values derived from the CT image analysis. However, it is notable that the values obtained from the single section lie within the range of possible variations of the parameters shown in Fig. 8. This observation implies that the discrepancy in values may be
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Table 1 Summary of air-void parameter equations from ASTM C457. Parameters Equations
Air content (%) A = (Ta/Tt) 100
Void frequency (mm n = N/Tt
1
)
Specific surface (mm–1)
Spacing factor (mm)
a = 4N/Ta
L = 3/a[1.4(l + Tp/Ta)1/3–1] when P/A > 4.342 L = Tp/4N when P/A < 4.342
(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 6. Distributions of air content and spacing factor of three specimens.
attributed to the heterogeneity of the air-void distribution in the specimens, thus substantiating our postulation that the parameters determined from a single section may be sensitive to the number of specimens and the location of specimens within the section. In addition, as shown in Table 2, the determination of air content from the single polished section failed to capture the higher airvoids entrained in the AE-2 specimens, presumably due to the heterogeneous distribution of air-voids. 4.3. Validation of the parameters This section evaluates the validity of the air-void parameters obtained through X-ray CT imaging. The air content and the spac-
ing factors have an inversely proportional relationship with each other [6]. Although the relationship is not strongly correlated, it can serve as a useful guide for validating the values of the parameters obtained in this study. The relationship between air content and spacing factors derived from this study (solid symbols) was superimposed on the data from the literature (open symbols) [6,17,18], and, as is clearly shown in Fig. 9, the obtained values exhibit good quantitative agreement with previously reported data. The characterization of 3D air-void configuration by reconstructing sliced images allows for deriving the spacing factor in 3D space as originally suggested by Powers [11]. Indeed, the equations suggested in ASTM C457 (Table 1) were devised based on the principle of stereology that aims for the estimation of 3D
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 7. Variations in the mean and one standard deviation of air content and spacing factor according to the number of traverse lines i, for the three specimens.
characteristics based on a 2D examination when 3D information is not available. However, when 3D information is available, it is better to calculate the air-void parameters rather than estimate them via any hypothesis or employing any principles. The spacing factor was originally suggested as half the distance between two diagonally adjacent voids in a cubic assembly, assuming that all voids are mono-sized spheres equally spaced throughout the entire volume [11]. Obviously, this distance cannot be measured by conventional optical methods; hence the stereology-based linear-traverse method or modified count methods have been widely used to estimate the spacing factor. Unlike optical devices, however, X-ray CT enables air-voids to be characterized and then the spacing factor to be derived directly in 3D space. To calculate the spacing factor in 3D, the spatial configuration of the air-voids was quantified by stacking the binary air-void images, as shown in Fig. 10. It may be seen that the air-voids are randomly
distributed without spatial bias, and that the number of air-voids increases markedly by air entrainment. The air-void objects, each of which was composed by combining the interconnected air-void voxels, were quantitatively defined and the number of voxels for each air-void object was obtained. The total number of air-voids, mean volume, and equivalent diameter of the air-voids for each specimen are shown in Fig. 10. The distribution curves of the equivalent diameters of air-voids were well-fitted to the log-normal distribution. As expected, the total number of air-voids increased with the amount of AEAs. The 3D configuration of air-voids can be idealized by conceptualizing them as mono-dispersed spheres whose number and total volume are equivalent to the original air-voids randomly distributed in the specimens. To determine the spacing factor in 3D, spheres were equally spaced in a cubic volume, as illustrated in Fig. 11. The spacing factor can then be determined based on ideally
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(a) Non-AE
(b) AE-1
(c) AE-2
Fig. 8. Evolution of average air content and spacing factor with an increasing number of selected images j. The error bar denotes one standard deviation.
Table 2 Estimated air-void parameters. Specimen Non-AE AE-1 AE-2
VOI size (mm3)
Air content (%)
Void frequency (mm
255.353
3.780 (6.3) 6.902 (6.8) 8.271 (6.7)
0.128 (0.156) 0.326 (0.223) 0.508 (0.270)
1
)
Spacing factor (mm)
Specific surface (mm
0.535 (0.403) 0.263 (0.291) 0.190 (0.240)
16.367 (9.8) 21.152 (13.1) 25.113 (16.1)
1
)
Note: Values in parentheses are those estimated from optical-microscopic examination of a polished section.
re-distributed spheres by considering the definition of the spacing factor. Therefore, results for the spacing factors from the original definition derived from the 2D stereological method using a sufficient number of 2D section images were compared with the values from the direct 3D method described herein. It can be postulated that the conventional 2D stereological method is acceptable for estimating representative spacing factors, provided that a sufficient number of traverse lines and 2D sampling sections are used.
4.4. Discussion The capability of X-ray CT to quantify the distributions of airvoid parameters has a few important implications. First, it is important that X-ray CT can successfully capture the inherent heterogeneity of the air-void distribution with no further effort. Furthermore, variations in the parameters can be an effective means of verifying the consistency of the parameters. As a criterion for
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Fig. 9. Estimated air-void parameters in this study superimposed on previously reported data. Fig. 11. Comparison between spacing factors estimated by averaging the values from 2D cross-sections following ASTM C457 and obtained by calculating the distances between equivalent spherical voids rearranged in 3D space as suggested by Powers (1954).
(a) Non-AE
(a) AE-1
(b) AE-2
Fig. 10. 3D configuration of air-voids and the distribution of equivalent air-void diameters (5.3 mm 5.3 mm 8.9 mm).
K.Y. Kim et al. / Construction and Building Materials 37 (2012) 93–101
freeze–thaw durability, air content is often used instead of the spacing factor for the sake of convenience. Revisiting Fig. 8, one may observe a higher variation in air content than in the spacing factor estimated from the 2D sectional images. This observation indicates that the determination of air content from a few crosssectional images can be misleading. This finding agrees well with the experience of experts that many concrete specimens exhibit desirable air content even when their spacing factors exceed specified limits [6]. This study substantiates the argument that the spacing factor is a more reliable measure for evaluating the quality of the dissemination of air-voids in cement-based materials. In this regard, the quantification of air-void parameters using X-ray CT imaging is a promising option for improving the current system of air-void assessment. 5. Conclusions X-ray CT is an efficient tool in the nondestructive high-resolution characterization of the microstructural configuration of materials. This paper has described the application of X-ray CT imaging in determining the air-void parameters of cement-based materials. As opposed to the conventional method of viewing a 2D section through an optical microscope, the standardized stereological method (the linear-traverse method) was conducted on a large number set of CT images of three cement paste specimens prepared with different air-void content as generated by air entrainment. Then, the linear-traverse method was implemented for sets of sectional CT images of the prepared specimens. The results indicate that due to the inherent heterogeneity of air-void distribution, the parameters estimated are significantly affected by the number of traverse lines selected for a single section and the number of sectional images selected across the volume of specimens. However, it is also shown that using a sufficient number of traverse lines and sectional images allows the derivation of values that can be regarded as representative and reliable, based on comparisons with published data and with a spacing factor calculated based on the original definition of the spacing factor in 3D space. The analysis of sets of X-ray CT images was also able to capture quantitative variations in the air-void parameters as controlled by air entrainment. The advantages offered by the X-ray CT imaging method can be summarized as follows. First, the method requires no preliminary physical treatment of specimens, such as cutting and polishing. Second, the method can increase the representativeness of the parameters by minimizing sampling effects. Finally, the method enables quantification of the heterogeneity of air-void distribution. In consideration of the limitations of the CT device available for this study, the specimen sizes (and thus the applicable aggregates) for X-ray CT imaging were intentionally restricted to obtain high resolution imaging of air-void distribution. However, since the size requirements for representative specimens are dependent on the size of the aggregate involved, it should be noted that it is necessary to prepare the size of specimens in consideration of the size of the aggregates used. With this provision taken as uncontroversial, we believe the proposed X-ray CT based method to be valid. Admittedly, the practical size of a cored specimen (>10 cm) for a
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gravel aggregate needs to be taken into consideration in making the proposed method practically available, a matter which will be investigated and reported shortly. Further, it is anticipated that improvements in the resolution capabilities of X-ray CT images will provide additional intriguing possibilities for the enhancement of conventional methods of determining the air-void parameters of cement-based materials. Acknowledgements This work was supported by the New & Renewable Energy program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean Ministry of Knowledge Economy (No. 2010T100200494) and the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Nos. 2011-0022883, 2011-0005593). References [1] Federal Highway Administration (FHWA). Freeze–thaw resistance of concrete with marginal air content; 2006. [2] American Society of Testing and Materials (ASTM). ASTM C457: standard test method for microscopical determination of parameters of the air-void system in hardened. Concrete; 2010. [3] Aligizaki KK, Cady PD. Air content and size distribution of air voids in hardened cement pastes using the section-analysis method. Cem Concr Res 1999;29(2): 273–80. [4] Jakobsen UH, Pade C, Thaulow N, Brown D, Sahu S, Magnusson O, et al. Automated air void analysis of hardened concrete – a round robin study. Cem Concr Res 2006;36(8):1444–52. [5] Corr DJ, Juenger MCG, Monteiro PJM, Bastacky J. Investigating entrained air voids and Portland cement hydration with low-temperature scanning electron microscopy. Cem Concr Compos 2004;26(8):1007–12. [6] Pleau R, Pigeon M, Laurencot J-L. Some findings on the usefulness of image analysis for determining the characteristics of the air-void system on hardened concrete. Cem Concr Compos 2001;23(2–3):237–46. [7] Zalocha D, Kasperkiewicz J. Estimation of the structure of air entrained concrete using a flatbed scanner. Cem Concr Res 2005;35(10):2041–6. [8] Promentilla MAB, Sugiyama T, Shimura K. Three dimensional characterization of air void system in cement-based materials. In: 3rd Asian concrete federation (ACF) international conference, Ho Chi Minh, Vietnam; 2008. p. 940–7. [9] Wong RCK, Chau KT. Estimation of air void and aggregate spatial distributions in concrete under uniaxial compression using computer tomography scanning. Cem Concr Res 2005;35(8):1566–76. [10] Manahiloh KN, Muhunthan B, Kayhanian M, Gebremariam SY. X-ray computed tomography and nondestructive evaluation of clogging in porous concrete field samples. J Mater Civ Eng 2012;24(8):1103–9. [11] Powers TC. The air requirement of frost-resistant concrete. Proc Highway Res Board 1949;29:184–211. [12] Hiriyannaiah HP. X-ray computed tomography for medical imaging. Signal Process Mag, IEEE 1997;14(2):42–59. [13] Bernard D, Chirazi A. Numerically enhanced microtomographic imaging method using a novel ring artifact filter. In: Desrues J, Viggiani G, Besuelle P, editors. Advances in X-ray tomography for geomaterials; 2006. [14] Raven C. Numerical removal of ring artifacts in microtomography. Rev Sci Instrum 1998;69(8):2978–80. [15] Peterson K, Carlson J, Sutter L, Van Dam T. Methods for threshold optimization for images collected from contrast enhanced concrete surfaces for air-void system characterization. Mater Charact 2009;60(7):710–5. [16] Otsu N. A threshold selection method from gray-level histograms. Syst, Man Cyber, IEEE Trans 1979;9(1):62–6. [17] Pigeon M, Gagné R, Foy C. Critical air-void spacing factors for low water– cement ratio concretes with and without condensed silica fume. Cem Concr Res 1987;17(6):896–906. [18] Plante P, Pigeon M, Foy C. The influence of water-reducers on the production and stability of the air void system in concrete. Cem Concr Res 1989;19(4): 621–33.