Durability Assessment of Coarse Aggregates for Hot-Mix Asphalt in Maine

Abstract

In this study, micro-Deval and Los Angeles (LA) abrasion tests were used to evaluate the durability of 72 coarse aggregates used for hot-mix asphalt (HMA) in Maine. Aggregates used in HMA must be durable and resistant to abrasion and degradation. Material loss in HMA pavements has recently been observed by the Maine Department of Transportation. Aggregate degradation has been hypothesized as a possible contributor. The micro-Deval results showed no correlation with results from the LA abrasion; the range in values was quite large. Two alternative methods of analyzing micro-Deval results were employed to measure the change in gradation of aggregate samples. A relatively large portion of tested aggregate sources was found to degrade significantly in the micro-Deval test while having acceptable AASHTO micro-Deval loss values, presumably as the result of fracturing instead of abrasion. The weighted-average method and the area-between-curves method proved to be effective in measuring the change in particle size distribution not captured with the Method 1 micro-Deval loss value. In addition, a significant influence of initial grading size was found in all the micro-Deval data, with finer initial gradations producing higher loss values. The alternative analysis methods for micro-Deval are recommended for use in detecting degradation not captured by the traditional micro-Deval value.

Assessment of Crushing Potential of Coarse Aggregates for Asphalt Concrete Mixtures

“…To improve the mechanical characterization of aggregates with these test methods, alternative analysis methodologies have been investigated to quantify the properties of aggregates and their correlation to the performance of asphalt mixtures. Nener-Plante recommended an alternative analysis approach for the MDA test to measure the change in the gradation of aggregates samples as a different degradation indicator ( 15 ). Umar et al proposed a modified method for LAA tests to optimize testing time ( 14 ).…”