Abstract
Aggregate gradation and shape properties are known to affect pavement mechanistic response and performance significantly. Under repeated traffic loading, aggregate particles in pavement courses are routinely subjected to degradation through attrition, impact, grinding, and polishing mechanisms, which alter their shape and size properties. Machine vision provides an objective and quantitative measurement of aggregate particle shape or morphological properties, including flatness and elongation, angularity, and surface texture. This paper focuses on the effectiveness of two advanced and validated aggregate imaging systems: an enhanced University of Illinois aggregate image analyzer (E-UIAIA) and a second-generation aggregate imaging system (AIMS-II)—for capturing changes in shape and size properties of aggregate particles caused by breakage, abrasion, and polishing actions. The micro-Deval apparatus was used in the laboratory to evaluate field degradation and polishing resistance of 11 aggregate materials with different mineralogical properties, collected from throughout Illinois and neighboring states. More than 26,000 particles were scanned with both imaging systems at various time intervals, and changes in aggregate morphological indexes were recorded. Despite differences in image acquisition and processing capabilities, both E-UIAIA and AIMS-II successfully quantified changes in morphological properties of particles from the micro-Deval tests. However, AIMS-II more closely reflected historical data on aggregate frictional properties obtained by the Illinois Department of Transportation. The imaging results were used to develop regression-based statistical models for determining aggregate polishing and degradation trends by considering both rate and magnitude of changes in shape properties.
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