Convolutional neural networks (CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridges using test images

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Details

Published: Feb 01, 2023
Vol/Issue: 276
Pages: 115306
License: View

Authors

L

Longqian Chen

School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China; Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China

Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143

Department of Road Engineering, Southeast University, Nanjing, China

L

Lizhong Jiang

School of Civil Engineering, Central South University, Changsha, Hunan, China

Funding

National Natural Science Foundation of China Award: 228

National Key Research and Development Program of China Award: 2021YFB2600600

Central South University Award: 2019JZZ01

Ministry of Science and Technology of the People's Republic of China

Cite This Article

Longqian Chen, Wenxin Chen, Lu Wang, et al. (2023). Convolutional neural networks (CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridges using test images. Engineering Structures, 276, 115306. https://doi.org/10.1016/j.engstruct.2022.115306

Convolutional neural networks (CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridges using test images

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