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Abstract

To address the shortcomings of existing testing methods for anti-skid performance of asphalt pavement and friction prediction models, this paper adopted a non-contact measurement method for data collection and proposed a non-contact testing technology for anti-skid performance based on 3D laser imaging. It mainly analyzed the three-dimensional data of asphalt pavement collected by the LS-40 portable three-dimensional laser surface analyzer. Meanwhile, Fourier transform and high-low pass filters were employed to separate the macro and micro textures, and eight indicators were proposed for the separated macro and micro texture data. The indicators include mean profile depth (DMPD), height average (HA), root mean square (RRMS), skewness (Rsk), kurtosis (Rku), two points slope variance (V2pts), mean peak-to-valley height (Hz (DIN)), and averaging of the double amplitudes (Da). Finally, the indicators were combined with temperature and BPN data measured by a pendulum friction meter for model training. A total of 336 sets of data were collected, with 75% of the sample data adopted for random forest model training and 25% for testing. Finally, the R2 values of the training set and testing set reached 0.92 and 0.77 respectively. Importance analysis indicates that temperature and the traditional DMPD indicator are key factors affecting the friction prediction model, and the model's prediction accuracy is improved with both the macro and micro textures considered.

Publication Date

1-18-2024

DOI

10.14048/j.issn.1671-2579.2023.06.008

First Page

44

Last Page

50

Submission Date

March 2025

Reference

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