A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics lab and field pavement texture evaluation are lacking. TPIN prediction based on structural material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens road to conduct correlation clustering analysis representative metrics. We conducted an influence exclude macroscope detection macro deformation metrics’ effects (international roughness index, IRI, mean profile depth, MPD). The cluster results verified feasibility of evaluating wear, differentiating wear states. accuracy indicators was high (R2 = 0.9958), implying that it feasible predict level using changes can be simulated by laboratory wear.