标题/作者	年份
Computer vision-based concrete crack detection using U-net fully convolutional networks Z Liu, Y Cao, Y Wang, W Wang Automation in Construction 104, 129-139	2019
Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection L Sun, Z Shang, Y Xia, S Bhowmick, S Nagarajaiah Journal of Structural Engineering 146 (5), 04020073	2020
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network S Li, X Zhao, G Zhou Computer‐Aided Civil and Infrastructure Engineering 34 (7), 616-634	2019
Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review M Azimi, AD Eslamlou, G Pekcan Sensors 20 (10), 2778	2020
A review of computer vision–based structural health monitoring at local and global levels CZ Dong, FN Catbas Structural Health Monitoring 20 (2), 692-743	2021
A review on deep learning-based structural health monitoring of civil infrastructures XW Ye, T Jin, CB Yun Smart Struct. Syst 24 (5), 567-585	2019
Image‐based concrete crack assessment using mask and region‐based convolutional neural network B Kim, S Cho Structural Control and Health Monitoring 26 (8), e2381	2019
SDDNet: Real-time crack segmentation W Choi, YJ Cha IEEE Transactions on Industrial Electronics 67 (9), 8016-8025	2019
A systematic review of convolutional neural network-based structural condition assessment techniques S Sony, K Dunphy, A Sadhu, M Capretz Engineering Structures 226, 111347	2021
Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning D Kang, SS Benipal, DL Gopal, YJ Cha Automation in Construction 118, 103291	2020
Machine learning algorithms in civil structural health monitoring: A systematic review M Flah, I Nunez, W Ben Chaabene, ML Nehdi Archives of computational methods in engineering 28, 2621-2643	2021
Concrete bridge surface damage detection using a single‐stage detector C Zhang, C Chang, M Jamshidi Computer‐Aided Civil and Infrastructure Engineering 35 (4), 389-409	2020
Machine learning for crack detection: Review and model performance comparison YA Hsieh, YJ Tsai Journal of Computing in Civil Engineering 34 (5), 04020038	2020
Concrete crack detection using context‐aware deep semantic segmentation network X Zhang, D Rajan, B Story Computer‐Aided Civil and Infrastructure Engineering 34 (11), 951-971	2019
Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine P Chun, S Izumi, T Yamane Computer‐Aided Civil and Infrastructure Engineering 36 (1), 61-72	2021
Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network J Deng, Y Lu, VCS Lee Computer‐Aided Civil and Infrastructure Engineering 35 (4), 373-388	2020
Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning D Dais, IE Bal, E Smyrou, V Sarhosis Automation in Construction 125, 103606	2021
State of the art in defect detection based on machine vision Z Ren, F Fang, N Yan, Y Wu International Journal of Precision Engineering and Manufacturing-Green …	2022
Automated crack detection and measurement based on digital image correlation N Gehri, J Mata-Falcón, W Kaufmann Construction and Building Materials 256, 119383	2020
A cost effective solution for pavement crack inspection using cameras and deep neural networks Q Mei, M Gül Construction and Building Materials 256, 119397	2020

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