Pavement compactness estimation based on 3D pavement texture features

Abstract

Understanding the correlation between pavement compactness and pavement texture features aids in determining the range of compaction passes. This paper proposes an estimation model for pavement compactness based on 3D pavement features. The compaction process was simulated in laboratory for four different gradation types with varying compaction passes while 3D texture data were obtained. Parameters were calculated to interpret texture features. The Random Forest model and the Shapley additive explanations approach were used to explore the contribution of different feature parameters to the compactness prediction model for the feature selection. A polynomial linear model was proposed to predict the compactness using five selected parameters, which showed a good fit. It was also observed that Da and Spk make a more substantial contribution to the model. Additionally, an optimal compaction pass range considering compactness, texture depth, and temperature drop was proposed to support the control strategies in road compaction construction sites.