Comprehensive Evaluation of AIMS Texture, Angularity, and Dimension Measurements
Publication: Journal of Materials in Civil Engineering
Volume 22, Issue 4
Abstract
Aggregates are the most widely used construction materials in the world in structures built from both asphaltic and portland cement concrete composites. The performance of these composites is affected by aggregate shape characteristics (e.g., angularity, texture, and dimensions). The aggregate imaging system (AIMS) is a computer automated system that was developed to measure aggregate shape characteristics using digital camera images of aggregates. This paper addresses four issues concerning AIMS measurements: (1) enhanced ways of handling and classifying the large data sets typically generated; (2) enhanced automation in processing fine aggregate images that appear to contain touching particles; (3) an improved consideration of measurement variability or repeatability between different operators, different AIMS units, and for aggregate placement and orientation; and (4) comparison of AIMS dimensional measurements with true three-dimensional measurements using X-ray computed tomography on a large coarse aggregate data set. The various aggregate sets used in this study covered a broad range of angularity and texture. The AIMS measurements of the ratios of dimensions were found to have excellent agreement with the more accurate X-ray computed tomography values, but the measurements of the individual dimensions were systematically low by about 10%.
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References
Al-Rousan, T., Masad, E., Myers, L., and Spiegelman, C. (2005). “A new methodology for shape classification of aggregates.” Transp. Res. Rec. 1913, Transportation Research Board, Washington, D.C., 11–23.
ASTM. (1999). “Concrete and concrete-making materials.” ASTM annual book of standards, Vol. 04.03, West Conshohocken, Pa.
Bathina, M. (2005). “Quality analysis of the aggregate imaging system (AIMS) measurements.” MS thesis, Texas A&M Univ., College Station, Tex.
Chandan, C., Sivakumar, K., Masad, E., and Fletcher, T. (2004). “Application of imaging techniques to geometry analysis of aggregate particles.” J. Comput. Civ. Eng., 18(1), 75–82.
Cooley, L., Jr., and James, R. (2003). “Micro-Deval testing of aggregates in the southeast.” Transportation Research Record. 1837, Transportation Research Board, Washington, D.C., 73–79.
Devore, J. L. (2004). Probability and statistics for engineering and the sciences, Thomson, Belmont, Calif.
Douglas, J. F., and Garboczi, E. J. (1995). “Intrinsic viscosity and polarizability of particles having a wide range of shapes.” Adv. Chem. Phys., 91, 85–153.
Erdoğan, S. T. et al. (2006). “Three-dimensional shape analysis of coarse aggregates: Methodology and preliminary results on several different coarse aggregates and reference rocks.” Cem. Concr. Res., 36, 1619–1627.
Ferraris, C. F., De Larrard, F., and Martys, N. S. (2001). “Fresh concrete rheology.” Materials science of concrete, J. Skalny, ed., Vol. VI, American Ceramic Society, 215–241.
Flatt, R. J., Martys, N. S., and Bergstrom, L. (2004). “The rheology of cementitious materials.” MRS Bull., 29, 314–318.
Garboczi, E. J. (2002). “Three-dimensional mathematical analysis of particle shape using X-ray tomography and spherical harmonics: Application to aggregates used in concrete.” Cem. Concr. Res., 32, 1621–1638.
Garboczi, E. J., Douglas, J. F., and Bohn, R. B. (2006). “A hybrid finite element-analytical method for determining the intrinsic elastic moduli of particles having moderately extended shapes and a wide range of elastic properties.” Mech. Mater., 38, 786–800.
Kanungo, T., Mount, D. M., Netanyahu, N., Piatko, C., Silverman, R., and Wu, A. Y. (2002). “An efficient k-means clustering algorithm: Analysis and implementation.” IEEE Trans. Pattern Anal. Mach. Intell., 24, 881–892.
Lingras, P., and Huang, X. (2005). “Statistical, evolutionary, and neurocomputing clustering techniques: Cluster-based vs object-based approaches.” Artif. Intell. Rev., 23(1), 3–29.
Mahmoud, E., and Masad, E. (2007). “Experimental methods for the evaluation of aggregate resistance to polishing, abrasion and breakage.” J. Mater. Civ. Eng., 19(11), 977–985.
Masad, E. (2003). “The development of a computer controlled image analysis system for measuring aggregate shape properties.” NCHRP-IDEA Final Rep. No. 77, National Cooperative Highway Research Program, Washington, D.C.
Masad, E., Al-Rousan, T., Button, J., Little, D., and Tutumluer, E. (2007). “Test methods for characterizing aggregate shape, texture, and angularity.” NCHRP Rep. No. 555, National Cooperative Highway Research Program, Washington, D.C.
Masad, E., Saadeh, S., Al-Rousan, T., Garboczi, E. J., and Little, D. (2005). “Computations of particle surface characteristics using optical and X-ray CT images.” Comput. Mater. Sci., 34, 406–424.
McGahan, J. (2005). “The development of correlations between HMA pavement performance and aggregate shape properties.” MS thesis, Texas A&M Univ., College Station, Tex.
Rogers, C., Bailey, M., and Price, B. (1991). “Micro-Deval test for evaluating the quality of fine aggregate for concrete and asphalt.” Transportation Research Record. 1301, Transportation Research Board, Washington, D.C., 68–76.
Russ, J. C. (2007). The image processing handbook, 5th Ed., CRC, Boca Raton, Fla.
Taylor, M. A., Garboczi, E. J., Erdoğan, S. T., and Fowler, D. W. (2006). “Some properties of irregular particles in 3-D.” Powder Technol., 162, 1–15.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 22 • Issue 4 • April 2010
Pages: 369 - 379
Copyright
© 2010 ASCE.
History
Received: Dec 11, 2008
Accepted: Jun 12, 2009
Published online: Jul 3, 2009
Published in print: Apr 2010
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