Abstract

This study introduces a novel three-dimensional (3D) pavement macrotexture measurement technique using ISO 25178 Surface Volume Parameters (SVP). The method calculates pavement material volume and texture height using cutoff thresholds based on the cumulative probability density function of measured heights. The research aims to optimize these cutoff thresholds for accurate 3D analysis of pavement macrotexture. The study evaluates the performance of various 3D and 2D parameters—assessing their repeatability, reliability, and accuracy—across thirty pavement samples with diverse macrotexture conditions. Findings show that the 3D SVP method, with optimized cutoff thresholds, strongly correlates with sand patch mean texture depth (MTD) and exhibits high repeatability and robust prediction confidence while overcoming the practical limitations of the sand patch method. Compared to traditional 2D methods, such as the ASTM E1845 mean profile depth (MPD), the 3D SVP provides a significantly improved representation of pavement texture, being less sensitive to profile location and surface irregularities. Additionally, the study finds that parameters like arithmetic mean height and root mean deviation are inadequate for MTD representation and proposes a new equation for predicting MTD based on MPD for surfaces with MTD greater than 2.5 mm.

Department(s)

Civil, Architectural and Environmental Engineering

Keywords and Phrases

3D pavement texture; Digital Surface analysis; Mean texture depth; Pavement macrotexture; Surface texture; Surface volume parameter; Three-dimensional measurement

International Standard Serial Number (ISSN)

0950-0618

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

08 Nov 2024

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