A novel pavement mean texture depth evaluation strategy based on three-dimensional pavement data filtered by a new filtering approach. (15th December 2020) - Liang, Jia; Gu, Xingyu; Chen, Yizheng; Ni, Fujian; Zhang, Tianjie
Abstract: Automatic pavement inspection is a reliable strategy to evaluate pavement conditions. Generally, the skid resistance of asphalt pavement is employed to characterize the pavement surface characteristics. The mean texture depth (MTD) of pavement is a crucial metric to characterize the macrotexture of asphalt pavement. The primary aim of this article is to evaluate pavement MTD. To evaluate this parameter based on three-dimensional (3-D) point cloud data extracted from a self-developed 3-D detection system, a novel filtering method and reference surface integral method were proposed. The novel filtering method was first employed to eliminate the isolated noise of the extracted 3-D point cloud data and was compared with the primary surface profile (PSP)-based filtering method. Then, a 3-D virtual model of pavement texture was generated based on the filtered 3-D data. Finally, a novel method, the reference surface integral, was proposed to evaluate the pavement MTD. The evaluated results were compared with the results measured via manual sand-patch testing. The results show that the novel filtering method performs better filtering performance than PSP-based filtering. The error between the evaluated MTD and the measured MTD is within 3.28%, which demonstrates that the proposed filtering method and MTD evaluation method have robust advantages and can be employed to evaluate pavement performance.