Laboratory Evaluation of Aggregate Friction and Texture Properties. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Fernando, Emmanuel G.; Arambula, Edith; Hu, Sheng; Crockford, William
• Is Part Of: Transportation research record. Volume 2675:Issue 10(2021)
• Page Start: 1396
• Page End: 1405
• Publication Date: 2021-10
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981211016465
• Languages: English
• ISSNs: 0361-1981

Abstract

Aggregate shape, texture, and angularity are important physical properties for the development of high-quality adhesive systems with asphalt binder, and a good quality aggregate skeleton in asphalt and concrete mixtures. These properties are commonly measured using systems that employ images of aggregate samples taken with a digital camera. In practice, macro-texture measurements are commonly conducted nondestructively at highway speed using high-frequency lasers. There is a need to investigate the application of these optical sensors for measuring aggregate texture in the laboratory. Researchers used a laser-based scanning system to test aggregates from five sources before and after Micro-Deval abrasion. The aggregate specimens were embedded in a ring-shaped polyester material and tested with the aggregate ring texturing system (ARTS) along with the dynamic friction tester (DFT) to characterize micro-texture and frictional properties. Researchers analyzed the laboratory test data to evaluate the relationship between DFT friction at 60 km/h (DFT60 ) and the micro mean profile depth obtained with the ARTS. This analysis showed that the igneous and gravel aggregates had better micro-texture and frictional characteristics compared with the dolomite and limestone aggregates. The results from this implementation project showed the potential for using the ARTS to improve the Texas Department of Transportation's existing surface aggregate classification system. Further testing is needed to characterize aggregates from other sources and compile a more comprehensive and representative database with which to establish meaningful aggregate classification criteria that include surrogate statistics for expected field performance.