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Proceedings Paper

Relation between sensor measurement and volumetric method in texture depth measurements
Author(s): Huayang He; Yishu Zhou; Hongbo Guo
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Paper Abstract

Texture depth is defined as the deviations of the road surface profile from the datum plane. It is currently assessed by two methods--sensor measured texture depth (SMTD) and sand patch method (SPM). Many researchers did lots of experiments to study the correlation between these two methods, but ignored the waveform characteristic of the road surface profile. A mathematical model of the road surface profile was built in this paper to calculation of texture depth and compare the two methods. This paper studied the statistical relationship between sensor measured texture depth and sand patch method in different waveform characteristics, and found out that the frequency is the main factor. The results show that the two methods have excellent correlation at the same frequency, and the correlation coefficient R2 is equal to 1. Plates with same frequency were designed to conducted comparison experiments. The result verify the above conclusion. It shows good correlation between the laser detection technology and the volumetric patch technique. Since manufacturers mainly use sand patch method for experiments, the result provides a theoretical basis and technical support for factory inspection. Also, it can be used to get the characteristics of the pavement structure in return.

Paper Details

Date Published: 8 March 2017
PDF: 7 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102550S (8 March 2017); doi: 10.1117/12.2267953
Show Author Affiliations
Huayang He, Research Institute of Highway (China)
Yishu Zhou, Research Institute of Highway (China)
Hongbo Guo, Research Institute of Highway (China)


Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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