期刊
CONSTRUCTION AND BUILDING MATERIALS
卷 233, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2019.117352
关键词
Pavement distress detection; Nondestructive testing; Network in network; Ground penetrating radar; Signal analysis
资金
- Opening Foundation of Research and Development Center of Transport Industry of Technologies, Materials and Equipments of Highway Construction and Maintenance (Gansu Road & Bridge Construction Group) [GLKF201811]
- China Scholarship Council [CSC201801810108]
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. (C) 2019 Elsevier Ltd. All rights reserved.
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