关键词: 道路工程；结构裂缝；层间不连续；探地雷达；深度神经网络

Abstract: The automatic identification method for asphalt pavement structural damage based on ground penetrating radar (GPR) mapping and deep neural network has the problem of limited data volume and unbalanced distribution of types, and the accuracy and stability of identification still need to be improved. The pavement structural damage identification technology based on heterogeneous GPR mapping was proposed. GPR was used to collect structural cracks and interlayer discontinuous diseases of asphalt pavement, and the measured profile maps was obtained. Based on the time-domain finite difference method, the echo features of cracks and interlayer discontinuities in the homogeneous model were numerically simulated, and combined with the measured maps to form dataset 1#. The "asphalt-aggregate" two-phase medium model was constructed based on the CT scan images of core samples, and the echo features of cracks and interlayer discontinuities in the two-phase medium model were simulated, which was combined the measured maps to form dataset 2#. YOLO v5 deep neural network was trained by dataset 1# and 2#, respectively. The research results show that the mAP@0.5 tested in YOLO v5 model using datasets 1# and 2# are 93.79% and 96.33%, which demonstrates that heterogeneous mapping features can enrich network training samples and improve the identification accuracy of deep learning model.

Key words: highway engineering; structural crack; interlayer discontinuity; ground-penetrating radar; deep neural network