3D Voxel-Based Approach to Quantify Aggregate Angularity and Surface Texture
Publication: Journal of Materials in Civil Engineering
Volume 29, Issue 7
Abstract
Angularity and surface texture of aggregate particles are important morphological characteristics and have great influences on the performance of asphalt mix. Several approaches have been developed to characterize the angularity and surface texture of aggregate particles in the past decade. Most existing techniques are limited to the two-dimensional (2D) scheme. In this study, a three-dimensional (3D) approach is proposed to determine the angularity index (AI) and surface texture index (STI) of aggregates, namely the 3D Sobel-Feldman operation, which has not been well used in civil engineering. First, aggregate particles are subjected to X-ray scanning to obtain cross-sectional images, which are then processed and stacked to construct the 3D voxel-based images. Then the gradient vector of each voxel on the surfaces is calculated based on the 3D Sobel-Feldman operation, which was derived and verified in this study based on the 2D Sobel-Feldman operation. The results showed that the 3D Sobel-Feldman operation derived in this study is correct and feasible to be used to determine the 3D AI of aggregates. The AI is determined by the accumulative change of gradient vectors of some selected surface voxels. A pretreatment (dilation followed by erosion) is used to reduce the effect of surface texture on the AI calculation. The surface texture index is determined by the relative loss of voxels after a morphological opening (erosion followed by dilation) operation on the 3D image. User-written coding was used to deal with the massive calculation. A total of 15 aggregate particles were used as case study to investigate the feasibility of the proposed approach to measure the 3D AI and STI. Some factors that affect the AI and STI values were also discussed to find the optimum approach. The 3D STI results were also compared to the 2D results to justify the benefits of the 3D approach.
Get full access to this article
View all available purchase options and get full access to this article.
References
AASHTO. (2002). “Standard method of test for determining the percentage of fracture in coarse aggregate.” AASHTO TP-61, Washington, DC.
AASHTO. (2010). “Uncompacted void content of fine aggregate.” AASHTO T-304, Washington, DC.
ASTM. (2006). “Standard test methods for uncompacted void content of fine aggregate (as influenced by particle shape, surface texture, and grading).” ASTM C1252, West Conshohocken, PA.
ASTM. (2013). “Standard test method for determining the percentage of fractured particles in coarse aggregate.” ASTM D5821, West Conshohocken, PA.
Chandan, C., Sivakumar, K., Masad, E., and Fletcher, T. (2003). “Application of imaging techniques to geometry analysis of aggregate particles.” J. Comput. Civ. Eng., 18(1), 75–82.
Chen, S., Yang, X., You, Z., and Wang, M. (2016). “Innovation of aggregate angularity characterization using gradient approach based upon the traditional and modified Sobel operation.” Constr. Build. Mater., 120(1), 442–449.
Fletcher, T., and Masad, E. (2002). “AIMS: Aggregate imaging system for characterizing fine and coarse aggregate shape properties.” Int. Center for Aggregates Research 10th Annual Symp.: Aggregates—Asphalt Concrete, Portland Cement Concrete, Bases and Fines, International Center for Aggregates Research, Baltimore.
Gul, M., Catbas, F., and Hattori, H. (2013). “Image-based monitoring of open gears of movable bridges for condition assessment and maintenance decision making.” J. Comput. Civ. Eng., 29(2), .
Huang, H., and Tutumluer, E. (2014). “Image-aided element shape generation method in discrete-element modeling for railroad ballast.” J. Mater. Civ. Eng., 527–535.
Ishai, I., and Gellber, H. (1982). “Effect of geometric irregularity of aggregates on the properties and behavior of asphalt concrete.” J. Assoc. Asphalt Paving Technol., 51, 494–521.
Jin, C., Yang, X., and You, Z. (2015). “Automated real aggregate modelling approach in discrete element method based on X-ray computed tomography images.” Int. J. Pavement Eng., 1–14.
Kevern, J., Wang, K., and Schaefer, V. (2009). “Effect of coarse aggregate on the freeze-thaw durability of pervious concrete.” J. Mater. Civ. Eng., 469–475.
Kim, Y., Haft-Javaherian, M., and Castro, L. (2014). “Two-dimensional virtual microstructure generation of particle-reinforced composites.” J. Comput. Civ. Eng., .
Li, L., Chan, P., and Lytton, R. L. (1991). “Detection of thin cracks on noisy pavement images.” Transp. Res. Rec., 1191, 131–135.
Liu, Y., and You, Z. (2009). “Visualization and simulation of asphalt concrete with randomly generated three-dimensional models.” J. Comput. Civ. Eng., 340–347.
Liu, Y., Zhou, X., You, Z., Yao, S., Gong, F., and Wang, H. (2016). “Discrete element modeling of realistic particle shapes in stone-based mixtures through MATLAB-based imaging process and PFC5.0 suite-based FISH codes.” Transportation Research Board 95th Annual Meeting, Transportation Research Board, Washington, DC.
Masad, E. (2003). “The development of a computer controlled image analysis system for measuring aggregate shape properties.”, Transportation Research Board, Washington, DC.
Masad, E., and Button, J. W. (2000). “Unified imaging approach for measuring aggregate angularity and texture.” Comput.-Aided Civ. Infrastruct. Eng., 15(4), 273–280.
Masad, E., Olcott, D., White, T., and Tashman, L. (2001). “Correlation of fine aggregate imaging shape indices with asphalt mixture performance.” Transp. Res. Rec., 1757, 148–156.
Masad, E. A. (2004). “Aggregate imaging system (AIMS): Basics and applications.”, Federal Highway Administration, Washington, DC.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mimics [Computer software]. MaterialiseNV, Leuven, Belgium.
O’Byrne, M., Ghosh, B., Schoefs, F., and Pakrashi, V. (2014). “Regionally enhanced multiphase segmentation technique for damaged surfaces.” Comput.-Aided Civ. Infrastruct. Eng., 29(9), 644–658.
Pan, T., and Tutumluer, E. (2006). “Evaluation of visual based aggregate shape classifications using the University of Illinois aggregate image analyzer (UIAIA). Geo Shanghai Int. Conf., Pavement Mechanics and Performance, ASCE, Reston, VA, 203–211.
Pan, T., Tutumluer, E., and Carpenter, S. H. (2006). “Rutting behavior of NCAT pavement test track superpave asphalt mixes analyzed for aggregate morphology effects.” J. Assoc. Asphalt Paving Technol., 75, 1169–1194.
Praticò, F. G., Vaiana, R., and Iuele, T. (2015). “Macrotexture modeling and experimental validation for pavement surface treatments.” Constr. Build. Mater., 95(1), 658–666.
Rao, C., Tutumluer, E., and Kim, I. T. (2002). “Quantification of coarse aggregate angularity based on image analysis.” Transp. Res. Rec., 1787, 117–124.
Sobel, I., and Feldman, G. (1968). “A 3x3 isotropic gradient operator for image processing.” Stanford Artificial Intelligence Project, Stanford, CA.
Sun, W., Wang, L., and Tutumluer, E. (2012). “Image analysis technique for aggregate morphology analysis with two-dimensional Fourier transform method.” Transp. Res. Rec., 2267, 3–13.
Tafesse, S. (2010). “Physical properties of coarse particles in till coupled to bedrock composition based on new 3D image analysis method.”, Licentiate Thesis, Stockholm, Sweden.
Tafesse, S., Fernlund, J., and Bergholm, F. (2012). “Digital sieving-MATLAB based 3-D image analysis.” Eng. Geol., 137(1), 74–84.
Tafesse, S., Fernlund, J. M. R., Sun, W., and Bergholm, F. (2013). “Evaluation of image analysis methods used for quantification of particle angularity.” Sedimentology, 60(4), 1100–1110.
Tutumluer, E., Pan, T., and Carpenter, S. H. (2005). “Investigation of aggregate shape effects on hot mix performance using an image analysis approach.”, Federal Highway Administration, Washington, DC.
Wang, H., Bu, Y., Wang, Y., Yang, X., and You, Z. (2016). “The effect of morphological characteristic of coarse aggregates measured with fractal dimension on asphalt mixture’s high temperature performance.” Adv. Mater. Sci. Eng., 2016, 1–9.
Wang, L., Sun, W., Tutumluer, E., and Druta, C. (2013). “Evaluation of aggregate imaging techniques for quantification of morphological characteristics.” Transp. Res. Rec., 2335, 39–49.
Wang, L., Wang, X., Mohammad, L., and Abadie, C. (2005). “Unified method to quantify aggregate shape angularity and texture using Fourier analysis.” J. Mater. Civ. Eng., 498–504.
Yang, X., Dai, Q., You, Z., and Wang, Z. (2014). “Integrated experimental-numerical approach for estimating asphalt mixture induction healing level through discrete element modeling of a single-edge notched beam test.” J. Mater. Civ. Eng., 27(9), .
Yang, X., You, Z., and Hu, J. (2016a). “Three-dimensional finite-element modeling for asphalt concrete using visual cross-sectional imaging and indirect element meshing based on discrete-element models.” J. Mater. Civ. Eng., .
Yang, X., You, Z., Jin, C., and Wang, H. (2016b). “Aggregate representation for mesostructure of stone based materials using a sphere growth model based on realistic aggregate shapes.” Mater. Struct., 49(6), 2493–2508.
Yang, X., You, Z., Wang, Z., and Dai, Q. (2016c). “Review on heterogeneous model reconstruction of stone-based composites in numerical simulation.” Constr. Build. Mater., 117(1), 229–243.
Yu, H., and Shen, S. (2012). “Impact of aggregate packing on dynamic modulus of hot mix asphalt mixtures using three-dimensional discrete element method.” Constr. Build. Mater., 26(1), 302–309.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 29 • Issue 7 • July 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 8, 2016
Accepted: Oct 27, 2016
Published online: Mar 23, 2017
Published ahead of print: Mar 27, 2017
Published in print: Jul 1, 2017
Discussion open until: Aug 23, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.