Intelligent Evaluation of Permeability Function of Porous Asphalt Pavement Based on 3D Laser Imaging and Deep Learning

The permeability of porous asphalt pavements is a critical skid resistance indicator that directly influences driving safety on wet roads. To ensure permeability (water infiltration capacity), it is necessary to assess the degree of clogging in the pavement. This study proposes a permeability evaluation model for porous asphalt pavements based on 3D laser imaging and deep learning. The model utilizes a 3D laser scanner to capture the surface texture of the pavement, a pavement infiltration tester to measure the permeability coefficient, and a deep residual network (ResNet) to train the collected data. The aim is to explore the relationship between the 3D surface texture of porous asphalt and its permeability performance. The results demonstrate that the proposed algorithm can quickly and accurately identify the permeability of the pavement without causing damage, achieving an accuracy and F1-score of up to 90.36% and 90.33%, respectively. This indicates a significant correlation between surface texture and permeability, which could promote advancements in pavement permeability technology.