Pavement-distress detection using ground-penetrating radar and network in networks

Abstract

This study proposes a nondestructive testing technique for pavement distress detection using ground-penetrating radar and network in networks. Ground-penetrating radar signals are imported into two network-in-network structure as input data directly. The network in networks are used as deep learning models to distinguish abnormal signals, recognize distress types, and measure distress locations and sizes. A database with information from four highways is generated by a ground-penetrating radar with different transmitting frequencies and numbers of samples per trace. Then, the database is used to train, validate, and test the network in networks. The results show that the proposed method detects cracks, water-damage pits, and uneven settlements with 85.17% accuracy, 2.15 mm location errors, and reasonable stability. The proposed method was superior to other state-of-the-art techniques in terms of classification accuracy, location error, and stability. Additionally, the results show that this method overcomes the negative effect of transmitting frequencies in pavement distress detection using GPR data.