Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 186, Issue -, Pages 303-312Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2018.07.124
Keywords
Aggregate Imaging Measurement System; Aggregate characteristics; Angularity; Sphericity; Asphalt coverage ratio
Categories
Funding
- Natural Science Foundation of China [U1733121]
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With the rapid growth of traffic, asphalt pavement with well performance conditions is urgently needed. Maintenance and renovation consume large amounts of natural aggregates and generate large amounts of solid waste which are negative to environment. For improving the performance of asphalt pavement, this study identified the effect of single morphological variable on the adhesion of aggregate and the mechanical properties of asphalt mixture. In order to obtain aggregates of single morphological variable, 9.5-13.2 grade of limestone and basalt were studied and manually divided into 5 groups respectively according to aggregate angularity in the Aggregate Imaging Measurement System (AIMS) specification. AIMS was used to accurately quantify the morphological characteristics of aggregates. Aggregates adhered by asphalt and AC-13 asphalt mixture with different aggregates morphological characteristic were prepared for the measurement of road performance. Results show that there are close linear relationships between angularity or sphericity of aggregate and the asphalt-aggregate adhesive property. It was also found that the relationship between angularity or sphericity of aggregate and mechanical properties such as Marshall stability and flexural strength tends to rise first and then decrease, demonstrating that there are optimal angularity and sphericity for Marshall stability and flexural strength. Accurately understanding morphological characteristics of aggregates is of great significance to the initial design stage of pavement. Therefore, it is suggested that the digital aggregate morphology test could be included in the experimental specifications of aggregates for achieving better road performance. (C) 2018 Elsevier Ltd. All rights reserved.
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