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2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete
Construction and Building Materials 127 (2016) 30–42
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete Jianguo Han a,b,⇑, Kejin Wang b, Xuhao Wang c, Paulo J.M. Monteiro d a
Department of Civil Engineering, Tsinghua University, Beijing 100084, China Department of Civil, Construction and Environmental Engineering, Iowa State University, IA 50014, USA c National Concrete Pavement Technology Center, Institute for Transportation, IA 50014, USA d Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA b
h i g h l i g h t s Two-dimensional image analysis method for particle status analysis is developed. Coarse aggregate characteristic and distribution in concrete are analyzed. Information of 2D image analysis and 3D experiment results has good consistency. Image analysis can help to improve concrete quality and optimize mix proportion. Disturbance should be avoid to guarantee coarse aggregate distributing homogeneity.
a r t i c l e
i n f o
Article history: Received 19 March 2016 Received in revised form 31 July 2016 Accepted 28 September 2016 Available online 3 October 2016 Keywords: Concrete Image analysis Aggregate Grading Orientation Homogeneity Particle distance
a b s t r a c t Two-dimensional (2D) image analysis method is developed for evaluating coarse aggregate characteristic and distribution in concrete, based on concrete cross-section image. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio (MAAR). The 2D image analysis method is performed on one conventional concrete and four self-consolidating concrete. Except exhibiting the ability of the 2D image analysis method, the analysis results also show the consistency of 2D information and 3D information, by comparing particle grading with sieving curve, and MAAR with mortar to coarse aggregate volume ratio (MAVR). Meanwhile, the 2D image analysis method can reveal the influence stochastic error, such as vibration and non-uniform sampling, on the homogeneity of concrete. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The characteristic of aggregate, especially coarse aggregate, have significant effects on the properties of both fresh and hardened concrete. Well-graded and round coarse aggregate permits the lowering of paste volume required to obtain specified fresh concrete workability. Homogenously distributed and randomly oriented coarse aggregate can help to improve concrete mechanical properties, durability, volume stability, and impermeability. Because of the high flowability required for self-consolidation, mortar in self-consolidating concrete (SCC) usually has low yield
⇑ Corresponding author at: Department of Civil Engineering, Tsinghua University, Beijing 100084, China. E-mail address: [email protected] (J. Han). http://dx.doi.org/10.1016/j.conbuildmat.2016.09.120 0950-0618/Ó 2016 Elsevier Ltd. All rights reserved.
stress and viscosity, which may lead to coarse aggregate segregation. Using image analysis and rheology methods, some research has been done to understand the influencing factors and characterization methods of concrete segregation. Shen studied the segregation tendency of SCC under static and dynamic conditions, and developed a model to analyze the critical factors [1]. Using a two-dimensional (2D) image analysis method, Wang evaluated the influence of coarse aggregate size, mineral admixture, and viscosity modifying agent on coarse aggregate distribution and concrete segregation [2]. Using geoendoscopy and image processing techniques, Breul et al. developed a method to evaluate on-site concrete homogeneity [3]. Fang and Labi developed a methodology for recognizing aggregate particles in concrete and evaluating the Hardened Visual Stability Index (HVSI) of concrete using statistical methods [4]. Safiuddin studied the relationship between the fresh
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concrete passing ability and the segregating tendency, and concluded that the filling and passing ability of SCC concrete was inversely proportional to its segregation resistance [5]. Masad employed a 2D image analysis method to study the influence of different compaction methods on aggregate and air void distribution in asphalt concrete [6]. Using commercial image analysis software, Gopalakrishnan developed a macro subroutine to study the effect of consolidation on the coarse aggregate distribution and orientation in hot-mixed asphalt concrete [7,8]. Yue and Morin used the angle between the major axis of an aggregate particle and a selected axis to represent the particle orientation in asphalt concrete [9]. These studies have promoted the understanding of influencing factors on the segregation problem of concrete, as well as the use of the image analysis for aggregate particle discrimination and characterization. But there still lacks an image analysis method that can systematically characterize the coarse aggregate characteristic and distribution information in concrete, as well as a consistency investigation on the 2D image analysis method results with 3D results obtained with other measuring methods. In this paper, 2D image analysis method is developed that is capable of giving information about coarse aggregate characteristic and distribution in concrete. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio (MAAR). Meanwhile, this paper investigates the consistency of 2D image analysis results with the coarse aggregate sieving curve, mortar to coarse aggregate volume ratio (MAVR) calculated based on concrete mix proportion, and the segregation tendency predicted by rheology parameters.
2. Materials and mix proportions The cementitious materials used in the present study include Type I/II Portland cement, Class C fly ash (C-FA), Class F fly ash
(F-FA), ground granulated blast-furnace slag (slag), and limestone powder (LP). Their chemical compositions are listed in Table 1. River sand with a fineness modulus of 2.63 was used as fine aggregate, and crushed limestone with a nominal diameter of 4.75–9.0 mm was used as coarse aggregate. The following chemical admixtures were used: air-entraining agent (AEA), polycarboxylate-based high-range water reducer (HRWR), and viscosity-modifying admixture (VMA). As shown in Table 2, by controlling the dosage of HRWR and VMA, one conventional concrete (CC) and four SCC mix proportions were produced. The compressive strength of the concrete is also presented in Table 2.
3. Test methods Slump and setting time of concrete was determined according to ASTM C1611 [10] and ASTM C403 [11], respectively. Yield stress and viscosity of mortar were measured using a Brookfield rheometer following the procedure suggested by Lomboy et al.: at the beginning of the test, the spindle rotation increases from 0 to 0.2 rev/s over 180 s; the rotation is then kept at 0.2 rev/s for 60 s, followed by an increment in rotation from 0.2 to 100 rev/s over 60 s; finally, the rotation is reduced to 0 rev/s over the next 60 s [12]. The rheology parameter (yield torque) of concrete was measured using an IBB rheometer following the procedure suggested by Wang et al.: pre-shear the sample at 0.2 rev/s for 25 s and stop the rotation for 25 s, then increase the impeller speed linearly from 0 to 1 rev/s over 100 s and follow with a linear decrease in impeller speed linearly to zero over 100 s [13]. The concrete was cast into a 4 in. (diameter) 8 in. (height) cylinder, the CC was consolidated by rodding according to ASTM C31 [14], and the SCC was casted into a mold without vibration. All concrete specimens were demolded after 24 h. The compressive strength was measured at 56 days according to ASTM C39 [15], and specimens were split at 210 days according to ASTM C496 [16].
Table 1 Chemical composition of cementitious materials, wt%. Name
CaO
SiO2
Al2O3
Fe2O3
SO3
MgO
Na2O
K2O
P2O5
LOI
Total
Cement F-FA C-FA Slag
62.94 15.78 21.54 36.86
20.10 50.87 42.46 37.00
4.44 20.17 19.46 9.00
3.09 5.27 5.51 0.68
3.18 0.61 1.20 –
2.88 3.19 4.67 10.40
0.10 0.69 1.42 0.30
0.61 1.09 0.68 0.38
0.06 0.44 0.84 0.01
2.22 0.07 0.19 –
99.62 98.18 97.97 94.63
Table 2 Concrete mix proportions and compressive strength at 56 days.
*
Name
CC*
SCC*
CC-1
SCC-1
SCC-2
SCC-3
SCC-4
Cement (kg/m3) C-FA (kg/m3) F-FA (kg/m3) Slag (kg/m3) LP (kg/m3) Water (kg/m3) Binder** (kg/m3) W/B Sand (kg/m3) Aggregate (kg/m3) HRWA*** VMA*** AEA*** C.S.y /MPa
295 0 98 0 0 169 393 0.43 698 993 0 0 36 44.9
337 113 0 0 0 166 450 0.37 737 901 522 0 52 53.9
337 0 113 0 0 166 450 0.37 737 901 522 130 52 51.6
320 0 0 138 0 166 458 0.36 743 908 522 130 52 53.7
290 0 90 0 67 166 447 0.37 737 901 782 0 98 41.1
CC: conventional concrete; SCC: self-consolidating concrete. Binder includes cement, class C fly ash, class F fly ash, slag and limestone powder. *** The unit of HRWA, VMA and AEA is ml/(100 kg binder). y Compressive strength. **
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were imported into the developed image analysis program for processing and analyzing. The flow chart of the developed 2D image analysis method is given in Fig. 1. The flow chart can be divided into five sections: (1) image process section (procedures 1–5), (2) aggregate particle distributing homogeneity analysis section (procedures 6 and 7), (3) aggregate particle distance analysis section (procedures 8–10), (4) mortar to coarse aggregate area ratio analysis section (procedures 8, 9, and 11), and (5) aggregate size, grading and orientation analysis section (procedures 12 and 13).
4. Image analysis program When the coarse aggregate characteristic and distribution status in concrete is evaluated using a 2D image analysis method, it is important to understand its representation of threedimensional (3D) status. According to Cavalieri’s Principle, the area percentage in the cross-section of a sample is assumed to be equal to the volume percentage in the 3D representation [17]. Because it is much easier to get a 2D image than a 3D image of concrete, much research has been done based on 2D imaging, and the feasibility of using 2D imaging to represent information about a 3D object has also been discussed. Mouret et al. and Igarashi et al. have utilized the anhydrous phase area percentage obtained from 2D back scattered electron (BSE) images to calculate the degree of hydration of cement [18,19]. Using 2D scanning electron microscopy (SEM) images, Haha et al. have calculated the degree of alkali silica reaction, and the research results suggested that the degree of critical reaction coincides with crack initiation caused by microscopic expansion [20]. Research done by Masad et al. showed that asphalt concrete porosity data obtained from 2D X-ray tomography images compared well with that measured by the bulk specific gravity method [21]. Scrivener discussed the feasibility of using 2D image analysis and stated that it is reasonable to use the area percentage of a 2D image to represent the 3D volume percentage [22]. In this paper, it is assumed that 2D image analysis results of coarse aggregate status can represent its 3D status. A 2D image analysis method was developed in this paper to evaluate coarse aggregate characteristic and distribution in concrete. First, concrete cylinder specimens were split in half (In this paper, concrete cross-section was prepare by splitting other than sawing; because the roughness of fractured surface was also of interest for other investigation.). Second, the fractured surfaces of the split specimens were photographed using a digital camera (3456 2304 pixels) under uniform light. Then, the digital images
4.1. Image process The purpose of image process section is to obtain a high-quality binary digital image, which is the object of the subsequent analyses. The image process section includes procedures 1–5 in Fig. 1. In procedure 1, a digital picture of a split concrete surface is obtained using a digital camera, as shown in Fig. 2(a). The digital picture is RGB color type (composed by red, green and blue colors). In procedure 2, the RGB color digital picture is first converted to a grayscale image, then aggregate particles (including fine and coarse aggregate) are recognized based on grayscale value. During the aggregate recognition process, the grayscale image is converted to a binary image, which is composed of only two logical values, 0 and 1, where 1 means the original pixel grayscale value is higher than the selected threshold value and appears as a white color pixel in the binary image (threshold value was determined by Otsu’s method [23]). Here the aggregate particles are endowed with white color because they have higher grayscale value than that of cement paste. The binary image is shown in Fig. 2(b). In actual practice, to increase the contrast of cement paste and aggregate, some kind of dye such as phenolphthalein can be used to change the color of the cement paste based on its high alkalinity. However, due to the mineral variety of aggregate, some error will be produced inevitably during the aggregate recognition procedure
1# Digital Digital Photo Photo
Aggregate recognition
Aggregate recognition 2# (Fine (Fine and and Coarse Coarse Aggregate) Aggregate)
3# Fine Fine Aggregate Aggregate Elimination Elimination
Do Do Aggregate Aggregate need need Detaching? Detaching? g?
4#
Y
N 5#
6# Concrete Concrete Region Region Division Division
7#
Concrete Concrete homogeneity homogeneity analysis analysis
Detached Detached Aggregate Aggregate
8# Aggregate ggregate Centroid Centroid Recognization Recognization
9#
Delaunry Delaunry Triangulation Triangulation
10# Distance Distance of of Aggregate Aggregate
12#
13#
Mortar Mortar to to Aggregate Aggregate Ratio Ratio 11#
Fig. 1. Flow chart of image analysis program.
Surround Surround Aggregate Aggregate by by LAR LAR
Aggregate Aggregate Size Size and and Orientation Orientation
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Fig. 2. Images related to image process section.
based on the grayscale value. To enhance the recognition quality, an alternative method is to recognize the coarse aggregate manually, which can be time consuming. In this paper, aggregate recognition is first accomplished based on the grayscale values of cement paste and aggregate. The recognition effect is then further enhanced by manually checking and correcting, through the comparison of binary image and concrete specimen. Much research has been done on aggregate particle recognition within 2D concrete images, and it seems more work should be done in this domain. Diamond showed the difficulty in aggregate recognition due to the color variation between aggregate particles and even within a single particle [24]. Soroushian et al. and Ammouche et al. have utilized Wood’s metal and florescent dye to enhance the ability to identify cracks and pores in concrete [25,26]. Yang and Buenfeld developed an image processing method to enhance the ability to discriminate aggregate particles, combining greyscale threshold value deciding, filtering, and binary operations [27]. Mouret et al. used an iterative self-organizing data analysis technique (ISODATA) to enhance the ability to discriminate aggregate [18]. However, due to the tremendous color variation that comes from the aggregate composition’s complexity, there is still no an automatic aggregate particle discrimination method that can guarantee perfect recognition ability. Therefore, it is sometimes necessary to improve the recognition effect manually. The research results of Abbas et al. and Stroeven et al. have showed the challenge of particle recognition and the necessity of manual recognition [28,29]. In procedure 3, the recognized aggregate is divided into fine aggregate and coarse aggregate based on the area of white color regions. The area is calculated by multiplying the number of pixels
in the region by the area per pixel. Aggregates with an area less than a circle area with an equivalent diameter of 4.75 mm are defined as fine aggregate in this paper, according to ASTM C125 [30]. The fine aggregate is eliminated from the total aggregate because coarse aggregate is often of greater concern when considering aggregate characteristic and distribution. The binary image containing coarse aggregate only is given in Fig. 2(c). In procedure 4, the coarse aggregate separating status is checked and the agglomerated aggregates are detached. The detaching process is accomplished by the following strategy: first, identify the centroids of the individual aggregate particles, as shown in Fig. 2(d); then, manually check whether two or more aggregate particles share a common centroid; if so, these aggregate particles are deemed as agglomerated and should be detached; finally, use an erosion algorithm to detach the agglomerate and once again detect the centroids of the aggregate particles to verify the detaching effect. The agglomerated aggregate particles and the detaching effect are illustrated in Fig. 2(e) and (f), respectively. Meanwhile, if there is aggregate agglomerate, after the detaching, it is very likely that an aggregate particle with an area less than a circle area with an equivalent diameter of 4.75 mm will be generated. So, procedure 3 is run one more time after the detaching to eliminate the generated fine aggregate. Arriving at procedure 5, only well-detached coarse aggregates are left and ready for the subsequent image analyses. 4.2. Coarse aggregate distributing homogeneity SCC usually has low yield stress and viscosity, due to the requirement of high flowability. Coarse aggregate tends to descend
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(or settle down) in a freshly mixed concrete, due to its higher density compared with paste, resulting in less coarse aggregate in the upper part and more in the lower part of a concrete element (segregation). As shown in Fig. 1, the aggregate particle distributing homogeneity analysis section consists of procedures 6 and 7. In procedure 6, the fractured concrete surface is divided into several regions along the casting direction. Coarse aggregate area ratio (AAR) is used to reflect the coarse aggregate distributing homogeneity in this paper. AAR is defined as the percentage of the total area of coarse aggregate to the total area of the divided region. Three regions are used in this paper, as illustrated in Fig. 3, which reflects the coarse aggregate distributing homogeneity in the upper, middle, and bottom parts of a concrete cylinder specimen
Fig. 3. Divided concrete fracture surface image.
Table 3 AAR value of divided regions of a concrete specimen. Location
Upper
Middle
Bottom
AAR (%)
20.1
21.4
24.8
(concrete was cast in from upper part and concrete specimen was hardened in vertical status). In procedure 7, the AAR value is calculated. Table 3 illustrates the calculation result of a concrete specimen. In a well prepared concrete specimen, the AAR values of all divided regions should be equal or very similar; otherwise, there is certain degree of segregation occurred in the measured concrete specimen. 4.3. Coarse aggregate characteristic The coarse aggregate characteristic section analyzes particle size, grading, roundness, and orientation. These parameters have significant effects on flowability and the consolidating ability of fresh concrete. Coarse aggregate particle size was measured by the least area rectangle (LAR) method, as shown in Fig. 4. In the LAR method, a rectangle is drawn around each coarse aggregate particle, and this rectangle has the smallest area compared with that of any other rectangles that could be drawn to surround the particle [31]. The concept of the LAR method is illustrated in Fig. 5(a). Keeping the particle still, a light source that can emit parallel light beams is moved in a circle around the particle, the parallel light beam is cast on the particle, and the shade length on the opposite side is measured. By recording the angle hi and length li, a series of (hi,li) pairs can be determined. The value of iis determined by the moving step of the light source, e.g., moving the light source every 1 degree in the range of 0–359 degrees will result in 360 pairs of (hi,li). By multiplying every two lengths that are perpendicular to each other, e.g., l0 l90 (the 0 degree length times the 90 degree length), a series of rectangular areas can be determined. By finding the rectangle with the minimum area from the list, the LAR of one coarse aggregate particle can be obtained. By repeating this procedure, all of the coarse aggregate particles’ LAR values can be obtained. To demonstrate how to calculate coarse aggregate particle size, roundness, and orientation, one of the coarse aggregate particles from Fig. 4, with its LAR marked as ‘‘A”, is taken out and drawn again in Fig. 5(b). Coarse aggregate size is defined as the longer side (L) of the LAR. Coarse aggregate roundness is defined as the ratio of the long side to the short side (L/W) of the LAR. The roundness is equal to one when the rectangle’s shape degenerates to a square. The higher the roundness, the more elongated the shape of the coarse aggregate. Coarse aggregate orientation is defined as the angle (h) formed by the longer side of the LAR and the casting direction of the concrete (the 8 in. long side of the concrete cylinder), and the orientation angle (h) is between 0 and 180 degree.
Fig. 4. Least area rectangles outside coarse aggregates, (color is used to differentiate aggregate particles). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Coarse aggregate size and orientation.
Fig. 6. Roundness and orientation of each coarse aggregate particle.
The roundness and orientation information of each coarse aggregate particle is given in Fig. 6, in which the radius gives the information about the roundness and the angle from 0 to 180 degrees gives the information about the orientation. Based on the results shown in Fig. 6, the statistical results of roundness can be calculated. The results show that 75.6% of the coarse aggregate has roundness between 1 and 2, 23.5% of the coarse aggregate has roundness between 2 and 3, and 0.9% of the coarse aggregate has roundness greater than 3. To further facilitate the understanding of the coarse aggregate orientation, the semicircle in Fig. 6 with the range of 0–180 degrees is divided to 6 regions, as illustrated in Fig. 7(a). It is defined that
0–30 and 150–180 degrees are horizontal directions, 30–60 and 120–150 degrees are tilt directions, 60–90 and 90–120 degrees are vertical directions. The orientation results of all coarse aggregate falling into the aforementioned three directions of the concrete specimen is given in Fig. 7(b). Individual coarse aggregate particle size of one concrete specimen is given in Fig. 8(a), and the horizontal axis is the coarse aggregate number. The statistical results of coarse aggregate particle size are given in Fig. 8(b), in which particle size is first sorted by value and classified into several bins (30 bins are used here). By adding step by step the values in consecutive bins along with the increment of coarse aggregate size in Fig. 8(b), the cumulative value of coarse aggregate counts along with particle size can be obtained, as given in Fig. 8(c). The shape and meaning of Fig. 8(c) is very similar to the coarse aggregate sieving curve, the difference being that the cumulative value here is coarse aggregate quantity rather than mass.
4.4. Distance between coarse aggregates The coarse aggregate particle distance analysis section includes procedures 8–10 in Fig. 1, and the distance of coarse aggregate particles is calculated based on the Delaunay triangle method [32]. Theoretically, for a set of points, the Delaunay triangle method tries to form a series of triangles, and for each triangle there is no other point that falls into its circumcircle. The Delaunay triangle
Fig. 7. Coarse aggregate orientation analyses.
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Fig. 8. (a) Coarse aggregate size distribution coarse aggregate size distribution and cumulative value.
Fig. 9. Delaunay triangles based on coarse aggregate centroids.
method is always unique as long as no four points in the point set are co-circular. Meanwhile, the Delaunay triangle method maximizes the minimum angle of all the angles of the triangles in the
triangulation process and tends to avoid skinny triangles. Based on its advantage for connecting points and dividing the space between points, the Delaunay triangle method is adopted here.
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In procedure 8, the centroids of each coarse aggregate particle are identified. In procedure 9, the Delaunay triangles are constructed based on the centroids. Delaunay triangulation can be accomplished by a software package (e.g. the Delaunay function in Matlab software), and the calculation result can tell which three points is connected to form a triangle. The Delaunay triangulation result based on the coarse aggregate centroids is shown in Fig. 9. The side length of a Delaunay triangle is defined as the distance between coarse aggregate particles. All of the Delaunay triangle side lengths are calculated, and the statistical result of these distances is given in Fig. 10(a), in which the distances are classified into a number of bins at discrete but consecutive distance values. The cumulative value of the coarse aggregate particle distances is given in Fig. 10(b), which can reflect the coarse aggregate particle distance distribution and can be used to compare this property among different concrete specimens. 4.5. Mortar to coarse aggregate area ratio The mortar to coarse aggregate area ratio can reflect the thickness of the mortar on the coarse aggregate surface. This value has decisive influence on concrete rheology, volume stability, mechanical properties, anti-penetration ability, and economical efficiency.
The mortar to coarse aggregate area ratio analysis section consists of procedures 8, 9, and 11 in Fig. 1. The Delaunay triangle method is used once again to calculate the mortar to coarse aggregate area ratio. As illustrated in Fig. 11(a), centroids C-1, C-2, and C-3 comprise a Delaunay triangle, in which the lined area corresponds to the mortar and the white colored areas correspond to coarse aggregate. By integrating the lined area and the white colored areas, the mortar to coarse aggregate area ratio can be calculated using Eq. (1).
Mortar to coarse aggregate area ratio ¼
dashed area white areas
ð1Þ
The mortar area and coarse aggregate area in a triangle is calculated using the following procedure: (1) build a rectangle just outside the specific triangle, as illustrated in Fig. 11(b), which can be done by finding the minimum and maximum coordinate values of the three vertexes of the triangle; (2) query all the pixels in the rectangle to judge which ones belong to the triangle; (3) if a pixel falls within the triangle, judge the binary value of the pixel, with 0 indicating mortar and 1 indicating aggregate; (4) count the number of the binary values 0 and 1 separately to calculate the mortar area and aggregate area.
Fig. 10. Coarse aggregate distance statistical analysis and cumulative value.
Fig. 11. Mortar to coarse aggregate area ratio analysis based on Delaunay triangle.
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Fig. 13. Concrete homogeneity analyses.
Fig. 12. Mortar to coarse aggregate area ratio of Delaunay triangle.
The calculated mortar to coarse aggregate area ratio values of each Delaunay triangle is given in Fig. 12, listed as the relationship between the Delaunay triangle number and the mortar to coarse aggregate area ratio. As can be seen from Fig. 12, except for the exceptionally high value at Delaunay triangle number 95, the values fluctuate in a relatively concentrated range. This figure also demonstrates the constructing ability of the Delaunay triangle method, which attempts to avoid building skinny triangles. 5. Analyses results and discussion Using the 2D image analysis method developed in this paper, one fractured concrete cross-section image of each concrete mix proportion as shown in Table 2 (one CC and four SCC) were analyzed and discussed as following. 5.1. Coarse aggregate distributing homogeneity Fig. 13 shows the analysis results of coarse aggregate distributing homogeneity. As can be seen, all of the concrete specimens have some tendency of segregation, with the bottom part possessing a higher AAR relative to the upper and middle parts. Compared with the conventional concrete specimens, the self-consolidating concrete specimens have better homogeneity except for specimen SCC-2. SCC-2 contains a relatively high level of Class F fly ash replacement of cement (25% by weight of total binder). Although VMA was added to its mix proportion, the anti-segregation ability of SCC-2 is still not as satisfying. Therefore, further measures such as adding more VMA or modifying it mix proportion should be adopted to tackle this problem. 5.2. Coarse aggregate particle grading and roundness Coarse aggregate particle grading and roundness were analyzed based on the LAR method. The results are shown in Fig. 14 (a) and (b), respectively. Integral curves are shown in Fig. 14 (a) and (b), and the integration method for particle roundness data is the same as that of particle size. A curve possessing higher slope in Fig. 14(a) indicates that there are more small particles in this specimen. Similarly, a curve possessing a higher slope in Fig. 14 (b) indicates that the corresponding specimen possesses more round-shaped particles than other specimens. All the concrete specimens in Fig. 14(a) and (b) were made with same batch of coarse aggregate, but the curves of coarse aggregate
size distribution and roundness are not exactly the same. This reflects the fact that coarse aggregate size and shape may vary in different specimens due to the inherent variation in coarse aggregate particle shape and size, and the different extraction location of coarse aggregate from its pile for concrete preparation can contribute to this result also. Based on the curves in Fig. 14(a) and (b), it can be concluded that the coarse aggregate in SCC-2 has better roundness and possess more small-sized particles than that of other specimens. Meanwhile, by constructing the relationship between coarse aggregate size and roundness in all concrete specimens, Fig. 14(c) clearly indicates that small-sized coarse aggregate tends to have better roundness. The comparison of coarse aggregate size distribution obtained from image analysis and the sieving curve is shown in Fig. 14(d). The sieving curve corresponds to the upper and right axis of Fig. 14(d), as indicated by the horizontal and vertical arrows. At the same time, the five image analysis results correspond to the right and bottom axis and are normalized by the coarse aggregate particle count of each specimen (as given in Fig. 14(a)). Although the sieving curve is based on mass percentage and the image analysis results are based on size percentage, their size distribution curves are quite close. Therefore, it seems very promising that coarse aggregate size distribution obtained by 2D image analysis can be used to represent or infer the coarse aggregate sieving curve. Fernlund et al. found similar results, indicating that the minimum bounding square around the minimum projected area of the particles showed good correlation with the sieve analysis results [33]. 5.3. Coarse aggregate orientation The coarse aggregate orientations for all concrete specimens are given in Fig. 15. Although it seems that the orientation status of specimen SCC-1 is more even than that of others, no definite orientation tendency can be concluded for all the other specimens. The result seems to indicate that the consolidating method used in this research did not jeopardize the orientation of the coarse aggregate very much. However, more work should be done to study the relationship between the difference of orientation and the index representing the orientation quality of concrete. 5.4. Distance between coarse aggregate particles The analysis results of coarse aggregate particle distance for all concrete specimens are given in Fig. 16, and the curves are in inte-
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Fig. 14. Coarse aggregate roundness and size distribution.
Fig. 15. Coarse aggregate orientation analysis results. Fig. 16. Coarse aggregate distance analysis results.
gral form. The slopes of all the curves become stable after 40 mm on the X-axis, meaning that coarse aggregate distance is seldom more than 40 mm when calculated by the Delaunay triangle
method. The slope of specimen SCC-2 is steeper than that of other specimens, meaning that the coarse aggregate particle distance in this specimen is shorter than that of others. This result is due to the
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fact that there are more small-sized and round-shaped particles in this specimen, and the segregation tendency of the specimen is more severe than other SCC concrete, which can enhance the packing density and lower the distance between the particles. 5.5. Mortar to coarse aggregate area ratio Fig. 17 shows the mortar to coarse aggregate area ratio for all concrete specimens. The curves become stable after the value of 5 on the X-axis, which means that the Delaunay triangles seldom contain 5 times more mortar area than coarse aggregate area. Once again, the curve for specimen SCC-2 has a steeper slope in the ascending section, which means that this specimen contains more Delaunay triangles possessing lower mortar to aggregate ratio than other specimens. The lower mortar to coarse aggregate area ratio is a result of the more compacted particles, which is reflected in the shorter coarse aggregate distance in Fig. 16. The average value of the mortar to coarse aggregate area ratio calculated by the Delaunay triangle method for all concrete specimens is given in Table 4. Meanwhile, the mortar to coarse aggregate area ratio calculated by the binary image area integration method and the mortar to coarse aggregate volume ratio calculated by the volume fraction method are also provided in Table 4. The binary image area integration method involves accumulating all the black points (representing mortar) and white points (representing coarse aggregate) in the binary image, to get the total mortar area and the total coarse aggregate area, and then dividing the total mortar area by the total coarse aggregate area. The volume fraction method involves first calculating the coarse aggregate volume in concrete, then assuming that the remaining volume is possessed by mortar, and finally calculating the mortar to coarse
aggregate volume ratio by dividing the mortar volume by the coarse aggregate volume. The coarse aggregate volume is obtained by dividing the coarse aggregate mass in the mix proportion by the coarse aggregate apparent density. As can be seen in Table 4, although there is deviation between different evaluating methods, the consistency of the results is good. The consistency between the value of the binary image area integration method and the volume fraction method shows the feasibility of using the area percentage obtained from a 2D image to represent the volume percentage in a 3D representation. The average value obtained from the Delaunay triangle method is not necessarily equal to the value obtained from the binary image area integration method. These two values can be equal only when the entire fractured surface is covered by Delaunay triangles and the coarse aggregate area ratio in each triangle is equal. The closer the two values and the lower the standard error of the Delaunay triangle area ratio values, the more homogeneous distribution status of coarse aggregate will be. The deviation between the 2D image analysis method and the volume calculation method may due to the following reasons: (1) the actual particle shape of the coarse aggregate is irregular; there are seldom volume-symmetrical particles, but abundant elongated particles; (2) during the splitting of the concrete, coarse aggregate tends to be fractured at its weak regions, and the weak region usually has a small cross-section area; (3) the concrete specimen performed image analysis contains less coarse aggregate content comparing with the theoretical value due to the bulk concrete segregation, as can be seen by the difference value (value in parenthesis) in Table 4 and the segregation status in Fig. 13; (4) there may be systematic error between cross-section area ratio and volume ratio, and this needs further research.
5.6. Concrete rheology and segregation Concrete workability and rheology properties are influenced by binder content, water to binder ratio, dosage of HRWA and VMA, and the characteristics of the coarse aggregate. In this section, the influence of coarse aggregate characteristics and concrete rheology on concrete segregation status is analyzed. The rheological properties of the conventional concrete and SCC are given in Table 5. To facilitate the explanation, some of the mix proportion parameters in Table 2 are presented in this table once again. The mortar used for rheology properties measurement was sieved out from the corresponding concrete. Compared with SCC, conventional concrete has lower slump flow value, higher yield stress of mortar, and higher yield torque of concrete than that of SCC. The viscosity of mortar form conventional concrete is comparable to that of SCC concrete. The main difference between the four types of SCC used in this research is that they contained different types of mineral admixtures, and VMA was introduced only to SCC-2 and SCC-3. Based on the mortar rheology parameters (yield stress and viscosity), it
Fig. 17. Mortar to aggregate area ratio analysis results.
Table 4 Mortar to coarse aggregate area/volume ratio. Type
Name
Mortar to coarse aggregate area ratio Delaunay triangle method*
* **
Mortar to coarse aggregate volume ratio Binary image area integration method **
Volume fraction method
CC
CC-1
1.34
1.34 (19.8% )
1.67
SCC
SCC-1 SCC-2 SCC-3 SCC-4
1.83 1.32 2.05 2.14
1.95 1.27 1.81 2.07
1.94 1.94 1.92 1.94
(0.5%) (34.5%) (5.7%) (6.7%)
Average value of all Delaunay triangles. Difference between binary image area integration method and volume fraction method.
41
J. Han et al. / Construction and Building Materials 127 (2016) 30–42 Table 5 Concrete workability, mortar and concrete rheology properties. Name
Slump flow (/mm) Final setting time/minutes Yield stress of mortar (/Pa) Viscosity of mortar (/Pas) Yield torque of concrete (/Nm) Binder (kg/m3) Mineral admixture type W/B HRWA VMA
CC
SCC
CC-1
SCC-1
SCC-2
SCC-3
SCC-4
165 298 142.05 1.56 2.84 393 F-FA 0.43 0 0
705 578 37.87 1.73 0.78 450 C-FA 0.37 522 0
730 490 26.80 0.87 0.61 450 F-FA 0.37 522 130
740 473 10.73 1.95 0.54 458 Slag 0.36 522 130
699 568 53.11 1.08 0.88 447 F-FA and LP 0.37 782 0
can be seen that SCC-2 and SCC-3 have lower yield stress values than that of SCC-1 and SCC-4, meanwhile, SCC-2 and SCC-4 have lower viscosity than SCC-1 and SCC-3. SCC-2 and SCC-3 concrete have lower yield torque values and higher slump flow values than that SCC-1 and SCC-4 concrete, and this is benefited from the lower yield stress values of corresponding mortar. The research of Hocˇevar et al. also indicated that there is a strong correlation between concrete yield stress and workability (slump and slump flow) [34]. Mortar is usually described as a plastic fluid using the Bingham model; its rheology behavior follows Eq. (2):
_ s ¼ s0 þ cg
ð2Þ
where, s is shear stress, s0 is yield stress, c_ is shear strain rate, and g is plastic viscosity. The descending tendency of a coarse aggregate particle in mortar is decided by the mortar yield stress, viscosity and density, particle size and shape, and its surrounding particles [35–37]. When the container of concrete is kept still and the movement of coarse aggregate is only impelled by gravity, the movement of fluid around a coarse aggregate particle can be regarded as streamlined flow. Postulating the shape of a coarse aggregate particle to be spherical, the threshold value of particle size to settle down given by Roussel N. is shown in Eq. (3) [38]:
dthreshold ¼
aggregate in all concrete specimens should not descend under gravity during the static curing period. So, the segregation of concrete specimen discussed in section 5.1 is caused other than gravity. During casting and rodding period, the mortar in concrete is disturbed and its yield stress is lowered, this effect can promote coarse aggregate to settle down. Meanwhile, if a coarse aggregate particle is hit directly by the head of the rod during rodding, it may be knocked downward drastically. The descending tendency of aggregate under the influence of consolidation has been reported by Petrou et al.; in their research, statically stilled aggregate was descended due to the vibration of mortar [34]. Given this information, the homogeneity problem in specimen CC-1 is more likely to be caused by the disturbance of the mortar during rodding. The segregation of SCC is most likely due to the disturbance of the mortar during casting and transferring; SCC-2 has more sever segregation tendency compared with other SCC is resulted from its lower mortar viscosity. Combining the information of 2D image analysis result and concrete rheology parameter, it is suggested that sever disturbance or vibration of fresh concrete, especially of SCC, should be avoid, for guaranteeing the coarse aggregate distributing homogeneity. 6. Conclusions
18s0 ðqs ql Þg
ð3Þ
where, dthreshold is the threshold value of coarse aggregate particle size, particle sizes smaller than which will not settle down in mortar; qs is the density of coarse aggregate; ql is the density of the mortar; and g is the gravity. The density of mortar is calculated by dividing the total mass of binder, water, and sand by their total volume in Table 2, as given in Table 6. By utilizing the yield stress and viscosity values of mortar in Table 5, and postulating the apparent density of coarse aggregate to be equal to 2650 kg/m3, the dthreshold can be calculated using Eq. (3) and given in Table 6. The maximum coarse aggregate size obtained from image analysis as shown in Fig. 14(a) is also provided in Table 6. Because the Dmax values are less than the dthreshold values in Table 6, the coarse
The 2D image analysis method developed in present study can be used to obtain information about coarse aggregate characteristic and distribution from cross-section image of concrete. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio. The information made available by the AAR value (particle distributing homogeneity), the LAR method (particle size, roundness and orientation), and the Delaunay triangle method (particle distance and mortar to coarse aggregate area ratio) can systematically reveal the status of coarse aggregate in concrete from 2D image.
Table 6 Threshold size of coarse aggregate for settling down. Type
Name
Mortar density* kg/m3
dthreshold mm
Dmax by image analysis mm
CC SCC
CC-1 SCC-1 SCC-2 SCC-3 SCC-4
2235 2276 2276 2295 2265
629 186 131 56 253
28 24 26 21 26
* For this calculation, the apparent density of cement, C-FA, F-FA, lime stone powder, slag, water and sand are postulated as 3150 kg/m3, 2600 kg/m3, 2600 kg/m3, 2650 kg/m3, 2900 kg/m3, 1000 kg/m3 and 2650 kg/m3, respectively.
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Concrete specimen from one conventional concrete and four SCC were investigated by the developed 2D image analysis method. And the image analysis results are compared with other experiment results, such as sieving curve and mortar to coarse aggregate volume ratio calculated from concrete mix proportion. There is good consistency between 2D information and 3D information. The 2D image analysis method can be used to evaluate concrete quality and optimize the mix proportion and casting methods, based on the information obtained from image analysis, fresh mortar/concrete rheology parameter and theoretical value calculated from mix proportion.
Acknowledgements The image analysis work was performed at Iowa State University (ISU) and the University of California, Berkeley (UCB). The first author would like to acknowledge the sponsorship from Tsinghua University of China for his visiting professorship at ISU and UCB, and the first author would like to acknowledge the National Natural Science Foundation of China (No. 51678343). The concrete property data used in this study were measured from a separate research project performed at ISU.
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[12] G. Lomboy, X. Wang, K. Wang, Rheological behavior and formwork pressure of SCC, SFSCC, and NC mixtures, Cem. Concr. Comp. (2014), http://dx.doi.org/ 10.1016/j.cemconcomp. 2014.05.001. [13] X. Wang, K. Wang, P. Taylor, Assessing particle packing based selfconsolidating concrete mix design method, Constr. Build. Mater. 70 (2014) 439–452. [14] ASTM C31/C31M, Standard Practice for Making and Curing Concrete Test Specimens in the Field, ASTM International, West Conshohocken, PA, 2012. [15] ASTM C39/C39M, Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, ASTM International, West Conshohocken, PA, 2014. [16] ASTM C496/C496M, Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens, ASTM International, West Conshohocken, PA, 2011. [17] A. Baddeley, E.B.V. Jensen, Stereology for Statisticians, Chapman and Hall/CRC, 2005, pp. 9–86. [18] M. Mouret, E. Ringot, A. Bascoul, Image analysis: a tool for the characterisation of hydration of cement in concrete–metrological aspects of magnification on measurement, Cem. Concr. Compos. 23 (2) (2001) 201–206. [19] S. Igarashi, A. Watanabe, M. Kawamura, Evaluation of capillary pore size characteristics in high-strength concrete at early ages, Cem. Concr. Res. 35 (3) (2005) 513–519. [20] M.B. Haha, E. Gallucci, A. Guidoum, et al., Relation of expansion due to alkali silica reaction to the degree of reaction measured by SEM image analysis, Cem. Concr. Res. 37 (8) (2007) 1206–1214. [21] E. Masad, B. Muhunthan, N. Shashidhar, et al., Internal structure characterization of asphalt concrete using image analysis, J. Comput. Civ. Eng. 13 (2) (1999) 88–95. [22] K.L. Scrivener, Backscattered electron imaging of cementitious microstructures: understanding and quantification, Cem. Concr. Compos. 26 (8) (2004) 935–945. [23] N. Otsu, A threshold selection method from gray-level histograms, Automatica 11 (285–296) (1975) 23–27. [24] S. Diamond, Considerations in image analysis as applied to investigations of the ITZ in concrete, Cem. Concr. Compos. 23 (2) (2001) 171–178. [25] P. Soroushian, M. Elzafraney, A. Nossoni, Specimen preparation and image processing and analysis techniques for automated quantification of concrete microcracks and voids, Cem. Concr. Res. 33 (12) (2003) 1949–1962. [26] A. Ammouche, J. Riss, D. Breysse, et al., Image analysis for the automated study of microcracks in concrete, Cem. Concr. Compos. 23 (2) (2001) 267–278. [27] R. Yang, N.R. Buenfeld, Binary segmentation of aggregate in SEM image analysis of concrete, Cem. Concr. Res. 31 (3) (2001) 437–441. [28] A. Abbas, G. Fathifazl, B. Fournier, et al., Quantification of the residual mortar content in recycled concrete aggregates by image analysis, Mater. Charact. 60 (7) (2009) 716–728. [29] P. Stroeven, A.P. Stroeven, D.H. Dalhuisen, Image analysis of natural’ concrete samples by automated and manual procedures, Cem. Concr. Compos. 23 (2) (2001) 227–236. [30] ASTM C125, Standard Terminology Relating to Concrete and Concrete Aggregates, ASTM International, West Conshohocken, PA, 2014. [31] H. Freeman, R. Shapira, Determining the minimum-area encasing rectangle for an arbitrary closed curve, Commun. ACM 18 (7) (1975) 409–413. [32] V.J.D. Tsai, Delaunay triangulations in TIN creation: an overview and a lineartime algorithm, Int. J. Geogr. Inform. Sci. 7 (6) (1993) 501–524. [33] J.M.R. Fernlund, R.W. Zimmerman, D. Kragic, Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size, Eng. Geol. 90 (3) (2007) 124–137. [34] A. Hocˇevar, F. Kavcˇicˇ, V. Bokan-Bosiljkov, Rheological parameters of fresh concrete – comparison of rheometers, Gradevinar 65 (02) (2013) 99–109. [35] A.N. Beris, J.A. Tsamopoulos, R.C. Armstrong, R.A. Brown, Creeping motion of a sphere through a Bingham plastic, J. Fluid Mech. 158 (1985) 219–244. [36] L. Shen, L. Struble, D. Lange, Modeling static segregation of self-consolidating concrete, ACI Mater. J. 106 (4) (2009) 367–374. [37] M. Petrou, B. Wan, F. Gardala-Maria, et al., Influence of mortar rheology on aggregate settlement, ACI Mater. J. 97 (4) (2000) 479–485. [38] N. Roussel, A theoretical frame to study stability of fresh concrete, Mater. Struct. 39 (1) (2006) 81–91.
Contents lists available at ScienceDirect
Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete Jianguo Han a,b,⇑, Kejin Wang b, Xuhao Wang c, Paulo J.M. Monteiro d a
Department of Civil Engineering, Tsinghua University, Beijing 100084, China Department of Civil, Construction and Environmental Engineering, Iowa State University, IA 50014, USA c National Concrete Pavement Technology Center, Institute for Transportation, IA 50014, USA d Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA b
h i g h l i g h t s Two-dimensional image analysis method for particle status analysis is developed. Coarse aggregate characteristic and distribution in concrete are analyzed. Information of 2D image analysis and 3D experiment results has good consistency. Image analysis can help to improve concrete quality and optimize mix proportion. Disturbance should be avoid to guarantee coarse aggregate distributing homogeneity.
a r t i c l e
i n f o
Article history: Received 19 March 2016 Received in revised form 31 July 2016 Accepted 28 September 2016 Available online 3 October 2016 Keywords: Concrete Image analysis Aggregate Grading Orientation Homogeneity Particle distance
a b s t r a c t Two-dimensional (2D) image analysis method is developed for evaluating coarse aggregate characteristic and distribution in concrete, based on concrete cross-section image. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio (MAAR). The 2D image analysis method is performed on one conventional concrete and four self-consolidating concrete. Except exhibiting the ability of the 2D image analysis method, the analysis results also show the consistency of 2D information and 3D information, by comparing particle grading with sieving curve, and MAAR with mortar to coarse aggregate volume ratio (MAVR). Meanwhile, the 2D image analysis method can reveal the influence stochastic error, such as vibration and non-uniform sampling, on the homogeneity of concrete. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The characteristic of aggregate, especially coarse aggregate, have significant effects on the properties of both fresh and hardened concrete. Well-graded and round coarse aggregate permits the lowering of paste volume required to obtain specified fresh concrete workability. Homogenously distributed and randomly oriented coarse aggregate can help to improve concrete mechanical properties, durability, volume stability, and impermeability. Because of the high flowability required for self-consolidation, mortar in self-consolidating concrete (SCC) usually has low yield
⇑ Corresponding author at: Department of Civil Engineering, Tsinghua University, Beijing 100084, China. E-mail address: [email protected] (J. Han). http://dx.doi.org/10.1016/j.conbuildmat.2016.09.120 0950-0618/Ó 2016 Elsevier Ltd. All rights reserved.
stress and viscosity, which may lead to coarse aggregate segregation. Using image analysis and rheology methods, some research has been done to understand the influencing factors and characterization methods of concrete segregation. Shen studied the segregation tendency of SCC under static and dynamic conditions, and developed a model to analyze the critical factors [1]. Using a two-dimensional (2D) image analysis method, Wang evaluated the influence of coarse aggregate size, mineral admixture, and viscosity modifying agent on coarse aggregate distribution and concrete segregation [2]. Using geoendoscopy and image processing techniques, Breul et al. developed a method to evaluate on-site concrete homogeneity [3]. Fang and Labi developed a methodology for recognizing aggregate particles in concrete and evaluating the Hardened Visual Stability Index (HVSI) of concrete using statistical methods [4]. Safiuddin studied the relationship between the fresh
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concrete passing ability and the segregating tendency, and concluded that the filling and passing ability of SCC concrete was inversely proportional to its segregation resistance [5]. Masad employed a 2D image analysis method to study the influence of different compaction methods on aggregate and air void distribution in asphalt concrete [6]. Using commercial image analysis software, Gopalakrishnan developed a macro subroutine to study the effect of consolidation on the coarse aggregate distribution and orientation in hot-mixed asphalt concrete [7,8]. Yue and Morin used the angle between the major axis of an aggregate particle and a selected axis to represent the particle orientation in asphalt concrete [9]. These studies have promoted the understanding of influencing factors on the segregation problem of concrete, as well as the use of the image analysis for aggregate particle discrimination and characterization. But there still lacks an image analysis method that can systematically characterize the coarse aggregate characteristic and distribution information in concrete, as well as a consistency investigation on the 2D image analysis method results with 3D results obtained with other measuring methods. In this paper, 2D image analysis method is developed that is capable of giving information about coarse aggregate characteristic and distribution in concrete. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio (MAAR). Meanwhile, this paper investigates the consistency of 2D image analysis results with the coarse aggregate sieving curve, mortar to coarse aggregate volume ratio (MAVR) calculated based on concrete mix proportion, and the segregation tendency predicted by rheology parameters.
2. Materials and mix proportions The cementitious materials used in the present study include Type I/II Portland cement, Class C fly ash (C-FA), Class F fly ash
(F-FA), ground granulated blast-furnace slag (slag), and limestone powder (LP). Their chemical compositions are listed in Table 1. River sand with a fineness modulus of 2.63 was used as fine aggregate, and crushed limestone with a nominal diameter of 4.75–9.0 mm was used as coarse aggregate. The following chemical admixtures were used: air-entraining agent (AEA), polycarboxylate-based high-range water reducer (HRWR), and viscosity-modifying admixture (VMA). As shown in Table 2, by controlling the dosage of HRWR and VMA, one conventional concrete (CC) and four SCC mix proportions were produced. The compressive strength of the concrete is also presented in Table 2.
3. Test methods Slump and setting time of concrete was determined according to ASTM C1611 [10] and ASTM C403 [11], respectively. Yield stress and viscosity of mortar were measured using a Brookfield rheometer following the procedure suggested by Lomboy et al.: at the beginning of the test, the spindle rotation increases from 0 to 0.2 rev/s over 180 s; the rotation is then kept at 0.2 rev/s for 60 s, followed by an increment in rotation from 0.2 to 100 rev/s over 60 s; finally, the rotation is reduced to 0 rev/s over the next 60 s [12]. The rheology parameter (yield torque) of concrete was measured using an IBB rheometer following the procedure suggested by Wang et al.: pre-shear the sample at 0.2 rev/s for 25 s and stop the rotation for 25 s, then increase the impeller speed linearly from 0 to 1 rev/s over 100 s and follow with a linear decrease in impeller speed linearly to zero over 100 s [13]. The concrete was cast into a 4 in. (diameter) 8 in. (height) cylinder, the CC was consolidated by rodding according to ASTM C31 [14], and the SCC was casted into a mold without vibration. All concrete specimens were demolded after 24 h. The compressive strength was measured at 56 days according to ASTM C39 [15], and specimens were split at 210 days according to ASTM C496 [16].
Table 1 Chemical composition of cementitious materials, wt%. Name
CaO
SiO2
Al2O3
Fe2O3
SO3
MgO
Na2O
K2O
P2O5
LOI
Total
Cement F-FA C-FA Slag
62.94 15.78 21.54 36.86
20.10 50.87 42.46 37.00
4.44 20.17 19.46 9.00
3.09 5.27 5.51 0.68
3.18 0.61 1.20 –
2.88 3.19 4.67 10.40
0.10 0.69 1.42 0.30
0.61 1.09 0.68 0.38
0.06 0.44 0.84 0.01
2.22 0.07 0.19 –
99.62 98.18 97.97 94.63
Table 2 Concrete mix proportions and compressive strength at 56 days.
*
Name
CC*
SCC*
CC-1
SCC-1
SCC-2
SCC-3
SCC-4
Cement (kg/m3) C-FA (kg/m3) F-FA (kg/m3) Slag (kg/m3) LP (kg/m3) Water (kg/m3) Binder** (kg/m3) W/B Sand (kg/m3) Aggregate (kg/m3) HRWA*** VMA*** AEA*** C.S.y /MPa
295 0 98 0 0 169 393 0.43 698 993 0 0 36 44.9
337 113 0 0 0 166 450 0.37 737 901 522 0 52 53.9
337 0 113 0 0 166 450 0.37 737 901 522 130 52 51.6
320 0 0 138 0 166 458 0.36 743 908 522 130 52 53.7
290 0 90 0 67 166 447 0.37 737 901 782 0 98 41.1
CC: conventional concrete; SCC: self-consolidating concrete. Binder includes cement, class C fly ash, class F fly ash, slag and limestone powder. *** The unit of HRWA, VMA and AEA is ml/(100 kg binder). y Compressive strength. **
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were imported into the developed image analysis program for processing and analyzing. The flow chart of the developed 2D image analysis method is given in Fig. 1. The flow chart can be divided into five sections: (1) image process section (procedures 1–5), (2) aggregate particle distributing homogeneity analysis section (procedures 6 and 7), (3) aggregate particle distance analysis section (procedures 8–10), (4) mortar to coarse aggregate area ratio analysis section (procedures 8, 9, and 11), and (5) aggregate size, grading and orientation analysis section (procedures 12 and 13).
4. Image analysis program When the coarse aggregate characteristic and distribution status in concrete is evaluated using a 2D image analysis method, it is important to understand its representation of threedimensional (3D) status. According to Cavalieri’s Principle, the area percentage in the cross-section of a sample is assumed to be equal to the volume percentage in the 3D representation [17]. Because it is much easier to get a 2D image than a 3D image of concrete, much research has been done based on 2D imaging, and the feasibility of using 2D imaging to represent information about a 3D object has also been discussed. Mouret et al. and Igarashi et al. have utilized the anhydrous phase area percentage obtained from 2D back scattered electron (BSE) images to calculate the degree of hydration of cement [18,19]. Using 2D scanning electron microscopy (SEM) images, Haha et al. have calculated the degree of alkali silica reaction, and the research results suggested that the degree of critical reaction coincides with crack initiation caused by microscopic expansion [20]. Research done by Masad et al. showed that asphalt concrete porosity data obtained from 2D X-ray tomography images compared well with that measured by the bulk specific gravity method [21]. Scrivener discussed the feasibility of using 2D image analysis and stated that it is reasonable to use the area percentage of a 2D image to represent the 3D volume percentage [22]. In this paper, it is assumed that 2D image analysis results of coarse aggregate status can represent its 3D status. A 2D image analysis method was developed in this paper to evaluate coarse aggregate characteristic and distribution in concrete. First, concrete cylinder specimens were split in half (In this paper, concrete cross-section was prepare by splitting other than sawing; because the roughness of fractured surface was also of interest for other investigation.). Second, the fractured surfaces of the split specimens were photographed using a digital camera (3456 2304 pixels) under uniform light. Then, the digital images
4.1. Image process The purpose of image process section is to obtain a high-quality binary digital image, which is the object of the subsequent analyses. The image process section includes procedures 1–5 in Fig. 1. In procedure 1, a digital picture of a split concrete surface is obtained using a digital camera, as shown in Fig. 2(a). The digital picture is RGB color type (composed by red, green and blue colors). In procedure 2, the RGB color digital picture is first converted to a grayscale image, then aggregate particles (including fine and coarse aggregate) are recognized based on grayscale value. During the aggregate recognition process, the grayscale image is converted to a binary image, which is composed of only two logical values, 0 and 1, where 1 means the original pixel grayscale value is higher than the selected threshold value and appears as a white color pixel in the binary image (threshold value was determined by Otsu’s method [23]). Here the aggregate particles are endowed with white color because they have higher grayscale value than that of cement paste. The binary image is shown in Fig. 2(b). In actual practice, to increase the contrast of cement paste and aggregate, some kind of dye such as phenolphthalein can be used to change the color of the cement paste based on its high alkalinity. However, due to the mineral variety of aggregate, some error will be produced inevitably during the aggregate recognition procedure
1# Digital Digital Photo Photo
Aggregate recognition
Aggregate recognition 2# (Fine (Fine and and Coarse Coarse Aggregate) Aggregate)
3# Fine Fine Aggregate Aggregate Elimination Elimination
Do Do Aggregate Aggregate need need Detaching? Detaching? g?
4#
Y
N 5#
6# Concrete Concrete Region Region Division Division
7#
Concrete Concrete homogeneity homogeneity analysis analysis
Detached Detached Aggregate Aggregate
8# Aggregate ggregate Centroid Centroid Recognization Recognization
9#
Delaunry Delaunry Triangulation Triangulation
10# Distance Distance of of Aggregate Aggregate
12#
13#
Mortar Mortar to to Aggregate Aggregate Ratio Ratio 11#
Fig. 1. Flow chart of image analysis program.
Surround Surround Aggregate Aggregate by by LAR LAR
Aggregate Aggregate Size Size and and Orientation Orientation
J. Han et al. / Construction and Building Materials 127 (2016) 30–42
33
Fig. 2. Images related to image process section.
based on the grayscale value. To enhance the recognition quality, an alternative method is to recognize the coarse aggregate manually, which can be time consuming. In this paper, aggregate recognition is first accomplished based on the grayscale values of cement paste and aggregate. The recognition effect is then further enhanced by manually checking and correcting, through the comparison of binary image and concrete specimen. Much research has been done on aggregate particle recognition within 2D concrete images, and it seems more work should be done in this domain. Diamond showed the difficulty in aggregate recognition due to the color variation between aggregate particles and even within a single particle [24]. Soroushian et al. and Ammouche et al. have utilized Wood’s metal and florescent dye to enhance the ability to identify cracks and pores in concrete [25,26]. Yang and Buenfeld developed an image processing method to enhance the ability to discriminate aggregate particles, combining greyscale threshold value deciding, filtering, and binary operations [27]. Mouret et al. used an iterative self-organizing data analysis technique (ISODATA) to enhance the ability to discriminate aggregate [18]. However, due to the tremendous color variation that comes from the aggregate composition’s complexity, there is still no an automatic aggregate particle discrimination method that can guarantee perfect recognition ability. Therefore, it is sometimes necessary to improve the recognition effect manually. The research results of Abbas et al. and Stroeven et al. have showed the challenge of particle recognition and the necessity of manual recognition [28,29]. In procedure 3, the recognized aggregate is divided into fine aggregate and coarse aggregate based on the area of white color regions. The area is calculated by multiplying the number of pixels
in the region by the area per pixel. Aggregates with an area less than a circle area with an equivalent diameter of 4.75 mm are defined as fine aggregate in this paper, according to ASTM C125 [30]. The fine aggregate is eliminated from the total aggregate because coarse aggregate is often of greater concern when considering aggregate characteristic and distribution. The binary image containing coarse aggregate only is given in Fig. 2(c). In procedure 4, the coarse aggregate separating status is checked and the agglomerated aggregates are detached. The detaching process is accomplished by the following strategy: first, identify the centroids of the individual aggregate particles, as shown in Fig. 2(d); then, manually check whether two or more aggregate particles share a common centroid; if so, these aggregate particles are deemed as agglomerated and should be detached; finally, use an erosion algorithm to detach the agglomerate and once again detect the centroids of the aggregate particles to verify the detaching effect. The agglomerated aggregate particles and the detaching effect are illustrated in Fig. 2(e) and (f), respectively. Meanwhile, if there is aggregate agglomerate, after the detaching, it is very likely that an aggregate particle with an area less than a circle area with an equivalent diameter of 4.75 mm will be generated. So, procedure 3 is run one more time after the detaching to eliminate the generated fine aggregate. Arriving at procedure 5, only well-detached coarse aggregates are left and ready for the subsequent image analyses. 4.2. Coarse aggregate distributing homogeneity SCC usually has low yield stress and viscosity, due to the requirement of high flowability. Coarse aggregate tends to descend
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(or settle down) in a freshly mixed concrete, due to its higher density compared with paste, resulting in less coarse aggregate in the upper part and more in the lower part of a concrete element (segregation). As shown in Fig. 1, the aggregate particle distributing homogeneity analysis section consists of procedures 6 and 7. In procedure 6, the fractured concrete surface is divided into several regions along the casting direction. Coarse aggregate area ratio (AAR) is used to reflect the coarse aggregate distributing homogeneity in this paper. AAR is defined as the percentage of the total area of coarse aggregate to the total area of the divided region. Three regions are used in this paper, as illustrated in Fig. 3, which reflects the coarse aggregate distributing homogeneity in the upper, middle, and bottom parts of a concrete cylinder specimen
Fig. 3. Divided concrete fracture surface image.
Table 3 AAR value of divided regions of a concrete specimen. Location
Upper
Middle
Bottom
AAR (%)
20.1
21.4
24.8
(concrete was cast in from upper part and concrete specimen was hardened in vertical status). In procedure 7, the AAR value is calculated. Table 3 illustrates the calculation result of a concrete specimen. In a well prepared concrete specimen, the AAR values of all divided regions should be equal or very similar; otherwise, there is certain degree of segregation occurred in the measured concrete specimen. 4.3. Coarse aggregate characteristic The coarse aggregate characteristic section analyzes particle size, grading, roundness, and orientation. These parameters have significant effects on flowability and the consolidating ability of fresh concrete. Coarse aggregate particle size was measured by the least area rectangle (LAR) method, as shown in Fig. 4. In the LAR method, a rectangle is drawn around each coarse aggregate particle, and this rectangle has the smallest area compared with that of any other rectangles that could be drawn to surround the particle [31]. The concept of the LAR method is illustrated in Fig. 5(a). Keeping the particle still, a light source that can emit parallel light beams is moved in a circle around the particle, the parallel light beam is cast on the particle, and the shade length on the opposite side is measured. By recording the angle hi and length li, a series of (hi,li) pairs can be determined. The value of iis determined by the moving step of the light source, e.g., moving the light source every 1 degree in the range of 0–359 degrees will result in 360 pairs of (hi,li). By multiplying every two lengths that are perpendicular to each other, e.g., l0 l90 (the 0 degree length times the 90 degree length), a series of rectangular areas can be determined. By finding the rectangle with the minimum area from the list, the LAR of one coarse aggregate particle can be obtained. By repeating this procedure, all of the coarse aggregate particles’ LAR values can be obtained. To demonstrate how to calculate coarse aggregate particle size, roundness, and orientation, one of the coarse aggregate particles from Fig. 4, with its LAR marked as ‘‘A”, is taken out and drawn again in Fig. 5(b). Coarse aggregate size is defined as the longer side (L) of the LAR. Coarse aggregate roundness is defined as the ratio of the long side to the short side (L/W) of the LAR. The roundness is equal to one when the rectangle’s shape degenerates to a square. The higher the roundness, the more elongated the shape of the coarse aggregate. Coarse aggregate orientation is defined as the angle (h) formed by the longer side of the LAR and the casting direction of the concrete (the 8 in. long side of the concrete cylinder), and the orientation angle (h) is between 0 and 180 degree.
Fig. 4. Least area rectangles outside coarse aggregates, (color is used to differentiate aggregate particles). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
J. Han et al. / Construction and Building Materials 127 (2016) 30–42
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Fig. 5. Coarse aggregate size and orientation.
Fig. 6. Roundness and orientation of each coarse aggregate particle.
The roundness and orientation information of each coarse aggregate particle is given in Fig. 6, in which the radius gives the information about the roundness and the angle from 0 to 180 degrees gives the information about the orientation. Based on the results shown in Fig. 6, the statistical results of roundness can be calculated. The results show that 75.6% of the coarse aggregate has roundness between 1 and 2, 23.5% of the coarse aggregate has roundness between 2 and 3, and 0.9% of the coarse aggregate has roundness greater than 3. To further facilitate the understanding of the coarse aggregate orientation, the semicircle in Fig. 6 with the range of 0–180 degrees is divided to 6 regions, as illustrated in Fig. 7(a). It is defined that
0–30 and 150–180 degrees are horizontal directions, 30–60 and 120–150 degrees are tilt directions, 60–90 and 90–120 degrees are vertical directions. The orientation results of all coarse aggregate falling into the aforementioned three directions of the concrete specimen is given in Fig. 7(b). Individual coarse aggregate particle size of one concrete specimen is given in Fig. 8(a), and the horizontal axis is the coarse aggregate number. The statistical results of coarse aggregate particle size are given in Fig. 8(b), in which particle size is first sorted by value and classified into several bins (30 bins are used here). By adding step by step the values in consecutive bins along with the increment of coarse aggregate size in Fig. 8(b), the cumulative value of coarse aggregate counts along with particle size can be obtained, as given in Fig. 8(c). The shape and meaning of Fig. 8(c) is very similar to the coarse aggregate sieving curve, the difference being that the cumulative value here is coarse aggregate quantity rather than mass.
4.4. Distance between coarse aggregates The coarse aggregate particle distance analysis section includes procedures 8–10 in Fig. 1, and the distance of coarse aggregate particles is calculated based on the Delaunay triangle method [32]. Theoretically, for a set of points, the Delaunay triangle method tries to form a series of triangles, and for each triangle there is no other point that falls into its circumcircle. The Delaunay triangle
Fig. 7. Coarse aggregate orientation analyses.
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Fig. 8. (a) Coarse aggregate size distribution coarse aggregate size distribution and cumulative value.
Fig. 9. Delaunay triangles based on coarse aggregate centroids.
method is always unique as long as no four points in the point set are co-circular. Meanwhile, the Delaunay triangle method maximizes the minimum angle of all the angles of the triangles in the
triangulation process and tends to avoid skinny triangles. Based on its advantage for connecting points and dividing the space between points, the Delaunay triangle method is adopted here.
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In procedure 8, the centroids of each coarse aggregate particle are identified. In procedure 9, the Delaunay triangles are constructed based on the centroids. Delaunay triangulation can be accomplished by a software package (e.g. the Delaunay function in Matlab software), and the calculation result can tell which three points is connected to form a triangle. The Delaunay triangulation result based on the coarse aggregate centroids is shown in Fig. 9. The side length of a Delaunay triangle is defined as the distance between coarse aggregate particles. All of the Delaunay triangle side lengths are calculated, and the statistical result of these distances is given in Fig. 10(a), in which the distances are classified into a number of bins at discrete but consecutive distance values. The cumulative value of the coarse aggregate particle distances is given in Fig. 10(b), which can reflect the coarse aggregate particle distance distribution and can be used to compare this property among different concrete specimens. 4.5. Mortar to coarse aggregate area ratio The mortar to coarse aggregate area ratio can reflect the thickness of the mortar on the coarse aggregate surface. This value has decisive influence on concrete rheology, volume stability, mechanical properties, anti-penetration ability, and economical efficiency.
The mortar to coarse aggregate area ratio analysis section consists of procedures 8, 9, and 11 in Fig. 1. The Delaunay triangle method is used once again to calculate the mortar to coarse aggregate area ratio. As illustrated in Fig. 11(a), centroids C-1, C-2, and C-3 comprise a Delaunay triangle, in which the lined area corresponds to the mortar and the white colored areas correspond to coarse aggregate. By integrating the lined area and the white colored areas, the mortar to coarse aggregate area ratio can be calculated using Eq. (1).
Mortar to coarse aggregate area ratio ¼
dashed area white areas
ð1Þ
The mortar area and coarse aggregate area in a triangle is calculated using the following procedure: (1) build a rectangle just outside the specific triangle, as illustrated in Fig. 11(b), which can be done by finding the minimum and maximum coordinate values of the three vertexes of the triangle; (2) query all the pixels in the rectangle to judge which ones belong to the triangle; (3) if a pixel falls within the triangle, judge the binary value of the pixel, with 0 indicating mortar and 1 indicating aggregate; (4) count the number of the binary values 0 and 1 separately to calculate the mortar area and aggregate area.
Fig. 10. Coarse aggregate distance statistical analysis and cumulative value.
Fig. 11. Mortar to coarse aggregate area ratio analysis based on Delaunay triangle.
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Fig. 13. Concrete homogeneity analyses.
Fig. 12. Mortar to coarse aggregate area ratio of Delaunay triangle.
The calculated mortar to coarse aggregate area ratio values of each Delaunay triangle is given in Fig. 12, listed as the relationship between the Delaunay triangle number and the mortar to coarse aggregate area ratio. As can be seen from Fig. 12, except for the exceptionally high value at Delaunay triangle number 95, the values fluctuate in a relatively concentrated range. This figure also demonstrates the constructing ability of the Delaunay triangle method, which attempts to avoid building skinny triangles. 5. Analyses results and discussion Using the 2D image analysis method developed in this paper, one fractured concrete cross-section image of each concrete mix proportion as shown in Table 2 (one CC and four SCC) were analyzed and discussed as following. 5.1. Coarse aggregate distributing homogeneity Fig. 13 shows the analysis results of coarse aggregate distributing homogeneity. As can be seen, all of the concrete specimens have some tendency of segregation, with the bottom part possessing a higher AAR relative to the upper and middle parts. Compared with the conventional concrete specimens, the self-consolidating concrete specimens have better homogeneity except for specimen SCC-2. SCC-2 contains a relatively high level of Class F fly ash replacement of cement (25% by weight of total binder). Although VMA was added to its mix proportion, the anti-segregation ability of SCC-2 is still not as satisfying. Therefore, further measures such as adding more VMA or modifying it mix proportion should be adopted to tackle this problem. 5.2. Coarse aggregate particle grading and roundness Coarse aggregate particle grading and roundness were analyzed based on the LAR method. The results are shown in Fig. 14 (a) and (b), respectively. Integral curves are shown in Fig. 14 (a) and (b), and the integration method for particle roundness data is the same as that of particle size. A curve possessing higher slope in Fig. 14(a) indicates that there are more small particles in this specimen. Similarly, a curve possessing a higher slope in Fig. 14 (b) indicates that the corresponding specimen possesses more round-shaped particles than other specimens. All the concrete specimens in Fig. 14(a) and (b) were made with same batch of coarse aggregate, but the curves of coarse aggregate
size distribution and roundness are not exactly the same. This reflects the fact that coarse aggregate size and shape may vary in different specimens due to the inherent variation in coarse aggregate particle shape and size, and the different extraction location of coarse aggregate from its pile for concrete preparation can contribute to this result also. Based on the curves in Fig. 14(a) and (b), it can be concluded that the coarse aggregate in SCC-2 has better roundness and possess more small-sized particles than that of other specimens. Meanwhile, by constructing the relationship between coarse aggregate size and roundness in all concrete specimens, Fig. 14(c) clearly indicates that small-sized coarse aggregate tends to have better roundness. The comparison of coarse aggregate size distribution obtained from image analysis and the sieving curve is shown in Fig. 14(d). The sieving curve corresponds to the upper and right axis of Fig. 14(d), as indicated by the horizontal and vertical arrows. At the same time, the five image analysis results correspond to the right and bottom axis and are normalized by the coarse aggregate particle count of each specimen (as given in Fig. 14(a)). Although the sieving curve is based on mass percentage and the image analysis results are based on size percentage, their size distribution curves are quite close. Therefore, it seems very promising that coarse aggregate size distribution obtained by 2D image analysis can be used to represent or infer the coarse aggregate sieving curve. Fernlund et al. found similar results, indicating that the minimum bounding square around the minimum projected area of the particles showed good correlation with the sieve analysis results [33]. 5.3. Coarse aggregate orientation The coarse aggregate orientations for all concrete specimens are given in Fig. 15. Although it seems that the orientation status of specimen SCC-1 is more even than that of others, no definite orientation tendency can be concluded for all the other specimens. The result seems to indicate that the consolidating method used in this research did not jeopardize the orientation of the coarse aggregate very much. However, more work should be done to study the relationship between the difference of orientation and the index representing the orientation quality of concrete. 5.4. Distance between coarse aggregate particles The analysis results of coarse aggregate particle distance for all concrete specimens are given in Fig. 16, and the curves are in inte-
J. Han et al. / Construction and Building Materials 127 (2016) 30–42
39
Fig. 14. Coarse aggregate roundness and size distribution.
Fig. 15. Coarse aggregate orientation analysis results. Fig. 16. Coarse aggregate distance analysis results.
gral form. The slopes of all the curves become stable after 40 mm on the X-axis, meaning that coarse aggregate distance is seldom more than 40 mm when calculated by the Delaunay triangle
method. The slope of specimen SCC-2 is steeper than that of other specimens, meaning that the coarse aggregate particle distance in this specimen is shorter than that of others. This result is due to the
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fact that there are more small-sized and round-shaped particles in this specimen, and the segregation tendency of the specimen is more severe than other SCC concrete, which can enhance the packing density and lower the distance between the particles. 5.5. Mortar to coarse aggregate area ratio Fig. 17 shows the mortar to coarse aggregate area ratio for all concrete specimens. The curves become stable after the value of 5 on the X-axis, which means that the Delaunay triangles seldom contain 5 times more mortar area than coarse aggregate area. Once again, the curve for specimen SCC-2 has a steeper slope in the ascending section, which means that this specimen contains more Delaunay triangles possessing lower mortar to aggregate ratio than other specimens. The lower mortar to coarse aggregate area ratio is a result of the more compacted particles, which is reflected in the shorter coarse aggregate distance in Fig. 16. The average value of the mortar to coarse aggregate area ratio calculated by the Delaunay triangle method for all concrete specimens is given in Table 4. Meanwhile, the mortar to coarse aggregate area ratio calculated by the binary image area integration method and the mortar to coarse aggregate volume ratio calculated by the volume fraction method are also provided in Table 4. The binary image area integration method involves accumulating all the black points (representing mortar) and white points (representing coarse aggregate) in the binary image, to get the total mortar area and the total coarse aggregate area, and then dividing the total mortar area by the total coarse aggregate area. The volume fraction method involves first calculating the coarse aggregate volume in concrete, then assuming that the remaining volume is possessed by mortar, and finally calculating the mortar to coarse
aggregate volume ratio by dividing the mortar volume by the coarse aggregate volume. The coarse aggregate volume is obtained by dividing the coarse aggregate mass in the mix proportion by the coarse aggregate apparent density. As can be seen in Table 4, although there is deviation between different evaluating methods, the consistency of the results is good. The consistency between the value of the binary image area integration method and the volume fraction method shows the feasibility of using the area percentage obtained from a 2D image to represent the volume percentage in a 3D representation. The average value obtained from the Delaunay triangle method is not necessarily equal to the value obtained from the binary image area integration method. These two values can be equal only when the entire fractured surface is covered by Delaunay triangles and the coarse aggregate area ratio in each triangle is equal. The closer the two values and the lower the standard error of the Delaunay triangle area ratio values, the more homogeneous distribution status of coarse aggregate will be. The deviation between the 2D image analysis method and the volume calculation method may due to the following reasons: (1) the actual particle shape of the coarse aggregate is irregular; there are seldom volume-symmetrical particles, but abundant elongated particles; (2) during the splitting of the concrete, coarse aggregate tends to be fractured at its weak regions, and the weak region usually has a small cross-section area; (3) the concrete specimen performed image analysis contains less coarse aggregate content comparing with the theoretical value due to the bulk concrete segregation, as can be seen by the difference value (value in parenthesis) in Table 4 and the segregation status in Fig. 13; (4) there may be systematic error between cross-section area ratio and volume ratio, and this needs further research.
5.6. Concrete rheology and segregation Concrete workability and rheology properties are influenced by binder content, water to binder ratio, dosage of HRWA and VMA, and the characteristics of the coarse aggregate. In this section, the influence of coarse aggregate characteristics and concrete rheology on concrete segregation status is analyzed. The rheological properties of the conventional concrete and SCC are given in Table 5. To facilitate the explanation, some of the mix proportion parameters in Table 2 are presented in this table once again. The mortar used for rheology properties measurement was sieved out from the corresponding concrete. Compared with SCC, conventional concrete has lower slump flow value, higher yield stress of mortar, and higher yield torque of concrete than that of SCC. The viscosity of mortar form conventional concrete is comparable to that of SCC concrete. The main difference between the four types of SCC used in this research is that they contained different types of mineral admixtures, and VMA was introduced only to SCC-2 and SCC-3. Based on the mortar rheology parameters (yield stress and viscosity), it
Fig. 17. Mortar to aggregate area ratio analysis results.
Table 4 Mortar to coarse aggregate area/volume ratio. Type
Name
Mortar to coarse aggregate area ratio Delaunay triangle method*
* **
Mortar to coarse aggregate volume ratio Binary image area integration method **
Volume fraction method
CC
CC-1
1.34
1.34 (19.8% )
1.67
SCC
SCC-1 SCC-2 SCC-3 SCC-4
1.83 1.32 2.05 2.14
1.95 1.27 1.81 2.07
1.94 1.94 1.92 1.94
(0.5%) (34.5%) (5.7%) (6.7%)
Average value of all Delaunay triangles. Difference between binary image area integration method and volume fraction method.
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J. Han et al. / Construction and Building Materials 127 (2016) 30–42 Table 5 Concrete workability, mortar and concrete rheology properties. Name
Slump flow (/mm) Final setting time/minutes Yield stress of mortar (/Pa) Viscosity of mortar (/Pas) Yield torque of concrete (/Nm) Binder (kg/m3) Mineral admixture type W/B HRWA VMA
CC
SCC
CC-1
SCC-1
SCC-2
SCC-3
SCC-4
165 298 142.05 1.56 2.84 393 F-FA 0.43 0 0
705 578 37.87 1.73 0.78 450 C-FA 0.37 522 0
730 490 26.80 0.87 0.61 450 F-FA 0.37 522 130
740 473 10.73 1.95 0.54 458 Slag 0.36 522 130
699 568 53.11 1.08 0.88 447 F-FA and LP 0.37 782 0
can be seen that SCC-2 and SCC-3 have lower yield stress values than that of SCC-1 and SCC-4, meanwhile, SCC-2 and SCC-4 have lower viscosity than SCC-1 and SCC-3. SCC-2 and SCC-3 concrete have lower yield torque values and higher slump flow values than that SCC-1 and SCC-4 concrete, and this is benefited from the lower yield stress values of corresponding mortar. The research of Hocˇevar et al. also indicated that there is a strong correlation between concrete yield stress and workability (slump and slump flow) [34]. Mortar is usually described as a plastic fluid using the Bingham model; its rheology behavior follows Eq. (2):
_ s ¼ s0 þ cg
ð2Þ
where, s is shear stress, s0 is yield stress, c_ is shear strain rate, and g is plastic viscosity. The descending tendency of a coarse aggregate particle in mortar is decided by the mortar yield stress, viscosity and density, particle size and shape, and its surrounding particles [35–37]. When the container of concrete is kept still and the movement of coarse aggregate is only impelled by gravity, the movement of fluid around a coarse aggregate particle can be regarded as streamlined flow. Postulating the shape of a coarse aggregate particle to be spherical, the threshold value of particle size to settle down given by Roussel N. is shown in Eq. (3) [38]:
dthreshold ¼
aggregate in all concrete specimens should not descend under gravity during the static curing period. So, the segregation of concrete specimen discussed in section 5.1 is caused other than gravity. During casting and rodding period, the mortar in concrete is disturbed and its yield stress is lowered, this effect can promote coarse aggregate to settle down. Meanwhile, if a coarse aggregate particle is hit directly by the head of the rod during rodding, it may be knocked downward drastically. The descending tendency of aggregate under the influence of consolidation has been reported by Petrou et al.; in their research, statically stilled aggregate was descended due to the vibration of mortar [34]. Given this information, the homogeneity problem in specimen CC-1 is more likely to be caused by the disturbance of the mortar during rodding. The segregation of SCC is most likely due to the disturbance of the mortar during casting and transferring; SCC-2 has more sever segregation tendency compared with other SCC is resulted from its lower mortar viscosity. Combining the information of 2D image analysis result and concrete rheology parameter, it is suggested that sever disturbance or vibration of fresh concrete, especially of SCC, should be avoid, for guaranteeing the coarse aggregate distributing homogeneity. 6. Conclusions
18s0 ðqs ql Þg
ð3Þ
where, dthreshold is the threshold value of coarse aggregate particle size, particle sizes smaller than which will not settle down in mortar; qs is the density of coarse aggregate; ql is the density of the mortar; and g is the gravity. The density of mortar is calculated by dividing the total mass of binder, water, and sand by their total volume in Table 2, as given in Table 6. By utilizing the yield stress and viscosity values of mortar in Table 5, and postulating the apparent density of coarse aggregate to be equal to 2650 kg/m3, the dthreshold can be calculated using Eq. (3) and given in Table 6. The maximum coarse aggregate size obtained from image analysis as shown in Fig. 14(a) is also provided in Table 6. Because the Dmax values are less than the dthreshold values in Table 6, the coarse
The 2D image analysis method developed in present study can be used to obtain information about coarse aggregate characteristic and distribution from cross-section image of concrete. Coarse aggregate characteristic includes particle size, particle grading, particle roundness and particle orientation. Coarse aggregate distribution includes particle distributing homogeneity, particle distance and mortar to coarse aggregate area ratio. The information made available by the AAR value (particle distributing homogeneity), the LAR method (particle size, roundness and orientation), and the Delaunay triangle method (particle distance and mortar to coarse aggregate area ratio) can systematically reveal the status of coarse aggregate in concrete from 2D image.
Table 6 Threshold size of coarse aggregate for settling down. Type
Name
Mortar density* kg/m3
dthreshold mm
Dmax by image analysis mm
CC SCC
CC-1 SCC-1 SCC-2 SCC-3 SCC-4
2235 2276 2276 2295 2265
629 186 131 56 253
28 24 26 21 26
* For this calculation, the apparent density of cement, C-FA, F-FA, lime stone powder, slag, water and sand are postulated as 3150 kg/m3, 2600 kg/m3, 2600 kg/m3, 2650 kg/m3, 2900 kg/m3, 1000 kg/m3 and 2650 kg/m3, respectively.
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Concrete specimen from one conventional concrete and four SCC were investigated by the developed 2D image analysis method. And the image analysis results are compared with other experiment results, such as sieving curve and mortar to coarse aggregate volume ratio calculated from concrete mix proportion. There is good consistency between 2D information and 3D information. The 2D image analysis method can be used to evaluate concrete quality and optimize the mix proportion and casting methods, based on the information obtained from image analysis, fresh mortar/concrete rheology parameter and theoretical value calculated from mix proportion.
Acknowledgements The image analysis work was performed at Iowa State University (ISU) and the University of California, Berkeley (UCB). The first author would like to acknowledge the sponsorship from Tsinghua University of China for his visiting professorship at ISU and UCB, and the first author would like to acknowledge the National Natural Science Foundation of China (No. 51678343). The concrete property data used in this study were measured from a separate research project performed at ISU.
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