Quantification of Segregation in Portland Cement Concrete Based on Spatial Distribution of Aggregate Size Fractions

Authors

DOI:

https://doi.org/10.5566/ias.2318

Keywords:

concrete, digital imaging, segregation, uniformity

Abstract

Segregation is one of the quality standards that must be monitored during the fabrication and placement of Portland cement concrete. Segregation refers to separation of coarse aggregate from the cement paste, resulting in inhomogeneous mixture. This study introduces a digital imaging based technique to quantify the segregation of Portland cement concrete from 2D digital images of cut sections. In the previous studies, segregation was evaluated based on the existence of coarse aggregate fraction at different geometrical regions of a sample cross section without considering its distribution characteristics. However, it is shown that almost all particle fractions can form clusters and increase the degree of segregation, thus deteriorating the structural performance of concrete. In the proposed methodology, a segregation index is developed by based on the spatial distribution of different size fractions of coarse aggregate within a sample cross section. It is shown that degradation in mixture’s homogeneity is controlled by the combined effect of particle distribution and their relative proportions in the mixture. Hence, a segregation index characterizing the mixture inhomogeneity is developed by considering not only spatial distribution of aggregate particles, but also their size fractions in the mixture. The proposed methodology can be successfully used as a quality control tool for monitoring the segregation level in hardened concrete samples.

References

Barbosa FS, Beaucour AL, Farage MC, Ortola S (2011). Image processing applied to the analysis of segregation in lightweight aggregate concretes. Constr Build Mater 25(8):3375-81.

https://doi.org/10.1016/j.conbuildmat.2011.03.028

Erdem S (2014). X-ray computed tomography and fractal analysis for the evaluation of segregation resistance, strength response and accelerated corrosion behavior of self-compacting lightweight concrete. Constr Build Mater 61:10-7.

https://doi.org/10.1016/j.conbuildmat.2014.02.070

Fang C, Labi S (2007). Image-processing technology to evaluate static segregation resistance of hardened self-consolidating concrete. Transp Res Rec 2020:1-9. http://dx.doi.org/10.3141/2020-01

Fernlund JM, Zimmerman RW, Kragic D (2007). Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size. Eng Geol 90(3-4):124-37.

https://doi.org/10.1016/j.enggeo.2006.12.007

Ferraris CF, Koehler E, Amziane S (2008). Report on measurements of workability and rheology of fresh concrete. ACI Committee 238, Report ACI 238.1R-08.

He, H, Courard, L, Pirard, E, Michel, F (2016). Shape analysis of fine aggregates used for concrete. Image Anal Stereol, 35(3): 159-66. https:/doi.org/10.5566/ias.1400

Johnson D, Johnson G, Robertson IN (2010). Quantifying segregation in self-consolidating concrete through image analysis. Department of Civil and Environmental Engineering, University of Hawaii, Research Report UHM/CEE/10-14.

Khayat KH, Vanhove Y, Pavate TV, Jolicoeur C (2007). Multi-electrode conductivity method to evaluate static stability of flowable and self-consolidating concrete. Mater J 104(4):424-33.

http://doi.org/10.14359/18833

Lee JR, Smith ML, Smith LN (2007). A new approach to the three-dimensional quantification of angularity using image analysis of the size and form of coarse aggregates. Eng Geol 91(2-4):254-64.

https://doi.org/10.1016/j.enggeo.2007.02.003

Mesbah HA, Yahia A, Khayat KH (2011). Electrical conductivity method to assess static stability of self- consolidating concrete. Cem Concr Res 41(5):451-58.

https://doi.org/10.1016/j.cemconres.2011.01.004

Mindess S, Young JF, Darwin D (1981). Concrete. Prentice-Hall, NJ.

Mora CF, Kwan AK (2000). Sphericity, shape factor, and convexity measurement of coarse aggregate for concrete using digital image processing. Cem Concr Res 30(3):351-58.

https://doi.org/10.1016/S0008-8846(99)00259-8

Navarrete I, Lopez M (2016). Estimating the segregation of concrete based on mixture design and vibratory energy. Constr Build Mater 122:384-90.

https://doi.org/10.1016/j.conbuildmat.2016.06.066

Nichols AB, Lange DA (2006). 3D surface image analysis for fracture modeling of cement-based materials. Cem Concr Res 36(6):1098-107. https://doi.org/10.1016/j.cemconres.2006.01.002

Ozen M, Guler M (2014). Assessment of optimum threshold and particle shape parameter for the image analysis of aggregate size distribution of concrete sections. Opt Lasers Eng 53:122- 32.

https://doi.org/10.1016/j.optlaseng.2013.08.020

Peterson K, Swartz R, Sutter L, Van Dam T (2001). Hardened concrete air void analysis with a flatbed scanner. Transp Res Rec 1775:36-43. https://doi.org/10.3141/1775-06

Solak AM, Tenza-Abril AJ, Baeza Brotons F (2018). Image analysis applications for the study of segregation in lightweight concretes. Int J Comput Methods Exp Meas 6(4):835-46.

https://doi.org/10.2495/CMEM-V6-N4-835-846

Yang XW, Man HG, Tian RL (2010). Deformation measurement of concrete by white light digital image analysis in frequency domain. Appl Mech Mater 29:496-01.

https://doi.org/10.4028/www.scientific.net/AMM.29-32.496

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Published

2020-11-25

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Section

Original Research Paper

How to Cite

Ozen, M., & Guler, M. (2020). Quantification of Segregation in Portland Cement Concrete Based on Spatial Distribution of Aggregate Size Fractions. Image Analysis and Stereology, 39(3), 147-159. https://doi.org/10.5566/ias.2318