Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

Abstract

To examine the three-dimensional texture structure of SMA-13 asphalt pavement and assess its anti-skid performance, a light gradient-boosting machine evaluation model was developed using non-contact three-dimensional laser-scanning technology. The study focused on collecting three-dimensional texture data from newly laid SMA-13 asphalt pavement. Subsequently, wavelet transform was employed to reconstruct the pavement’s three-dimensional texture, and discrete Fourier transform was utilized to separate macro- and microtextures, enabling the calculation of their characteristics. The macro- and micro-characteristics of the three-dimensional texture and friction coefficient were input into the model. A comparative analysis with linear regression and a random forest model revealed superior accuracy and efficiency in the model. The training set R2 is 0.948, and the testing set R2 is 0.842, effectively enabling the evaluation of pavement anti-skid performance. An analysis of parameter importance indicated that Rku and MPD are still effective indicators for evaluating skid resistance. Furthermore, diverse texture indexes exhibited varying effects on the anti-skid performance. The established asphalt pavement anti-skid evaluation model serves as a theoretical foundation for understanding the actual influence on pavement anti-skid performance.

Keywords

• road engineering
• asphalt pavement
• texture reconstruction
• machine learning
• skid resistance intelligent evaluation