• International Journal of Highway Engineering
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• v.9 no.4
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• pp.117-127
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• 2007

The exposed aggregate PCC(EAP) pavements have been successfully used in Europe and Japan as low-noise pavements. Coarse aggregate are exposed on the pavement surface texture of EAP by removing mortar of surface. The pavement surface texture should maintain not only low-noise characteristic but also adequate skid resistance level during the performance period. Skid resistance decreased with wearing and polishing of tire and pavement surface due to the repetition of tire-pavement contact. Since the tires mainly contact the exposed coarse aggregate, the shape and rock type of coarse aggregate significantly influence wearing and polishing of EAP pavements. The test for resistance to abrasion coarse aggregate by use of the Los Angeles machine(KS F 2508) and the method of test for resistance to abrasion coarse aggregate by use of the Accelerated Polishing Machine(ASTM D 3319-90) are generally used to evaluate polishing characteristics of aggregate. In this study, polishing of coarse aggregate of different five rock types were evaluated by KS F 2508(LA abrasion test) and ASTM D 3319-90(PSV method). The results of LA abrasion test and PSV method were contrary to each other. Since LA abrasion test is estimated the quantity of abrasion by the impact of aggregate, it may not be adequate to evaluate the polishing of aggregate by the repetition of tire. In the case of PSV method, the resistance of polishing is estimated the skid resistance variation of polished aggregate after repetition of tire. The PSV method is adequate for the evaluation on polishing of coarse aggregate. From the test results of PSV method, it was founded that rock type, specific gravity, coarse aggregate angularity, flat or elongated particles in coarse aggregate are significant to the resistance characteristic of coarse aggregate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.1-8
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• 2021

This study developed low-cost and high-efficiency pavement condition monitoring technology to produce the key information required for pavement management. A noise and artificial intelligence-based monitoring system was devised to compensate for the shortcomings of existing high-end equipment that relies on visual information and high-end sensors. From idea establishment to system development, functional definition, information flow, architecture design, and finally, on-site field evaluations were carried out. As a result, confidence in the high level of artificial intelligence evaluation was secured. In addition, hardware and software elements and well-organized guidelines on system utilization were developed. The on-site evaluation process confirmed that non-experts could easily and quickly investigate and visualized the data. The evaluation results could support the management works of road managers. Furthermore, it could improve the completeness of the technologies, such as prior discriminating techniques for external conditions that are not considered in AI learning, system simplification, and variable speed response techniques. This paper presents a new paradigm for pavement monitoring technology that has lasted since the 1960s.