| Computer vision and deep learning–based data anomaly detection method for structural health monitoring Y Bao, Z Tang, H Li, Y Zhang Structural Health Monitoring 18 (2), 401-421, 2019 | 501 | 2019 |
| The state of the art of data science and engineering in structural health monitoring Y Bao, Z Chen, S Wei, Y Xu, Z Tang, H Li Engineering 5 (2), 234-242, 2019 | 371 | 2019 |
| Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring Z Tang, Z Chen, Y Bao, H Li Structural Control and Health Monitoring 26 (1), e2296, 2019 | 325 | 2019 |
| Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images Y Xu, Y Bao, J Chen, W Zuo, H Li Structural Health Monitoring 18 (3), 653-674, 2019 | 261 | 2019 |
| Machine learning paradigm for structural health monitoring Y Bao, H Li Structural health monitoring 20 (4), 1353-1372, 2021 | 209 | 2021 |
| Compressive sampling for accelerometer signals in structural health monitoring Y Bao, JL Beck, H Li Structural Health Monitoring 10 (3), 235-246, 2011 | 195 | 2011 |
| Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network Y Xu, S Wei, Y Bao, H Li Structural Control and Health Monitoring 26 (3), e2313, 2019 | 175 | 2019 |
| Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring Y Bao, H Li, X Sun, Y Yu, J Ou Structural Health Monitoring 12 (1), 78-95, 2013 | 163 | 2013 |
| Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio S Li, S Wei, Y Bao, H Li Engineering Structures 155, 1-15, 2018 | 124 | 2018 |
| Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring Z Zou, Y Bao, H Li, BF Spencer, J Ou IEEE Sensors Journal 15 (2), 797-808, 2014 | 124 | 2014 |
| Identification of time‐varying cable tension forces based on adaptive sparse time‐frequency analysis of cable vibrations Y Bao, Z Shi, JL Beck, H Li, TY Hou Structural Control and Health Monitoring 24 (3), e1889, 2017 | 106 | 2017 |
| Fractal dimension‐based damage detection method for beams with a uniform cross‐section H Li, Y Huang, J Ou, Y Bao Computer‐Aided Civil and Infrastructure Engineering 26 (3), 190-206, 2011 | 105 | 2011 |
| Optimal policy for structure maintenance: A deep reinforcement learning framework S Wei, Y Bao, H Li Structural Safety 83, 101906, 2020 | 97 | 2020 |
| Selection of regularization parameter for l1-regularized damage detection R Hou, Y Xia, Y Bao, X Zhou Journal of sound and vibration 423, 141-160, 2018 | 95 | 2018 |
| An active learning method combining deep neural network and weighted sampling for structural reliability analysis Z Xiang, J Chen, Y Bao, H Li Mechanical Systems and Signal Processing 140, 106684, 2020 | 94 | 2020 |
| Structural damage identification based on integration of information fusion and shannon entropy H Li, Y Bao, J Ou Mechanical Systems and Signal Processing 22 (6), 1427-1440, 2008 | 89 | 2008 |
| Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology Z Chen, H Li, Y Bao, N Li, Y Jin Structural Control and Health Monitoring 23 (3), 517-534, 2016 | 83 | 2016 |
| Compressive sensing‐based lost data recovery of fast‐moving wireless sensing for structural health monitoring Y Bao, Y Yu, H Li, X Mao, W Jiao, Z Zou, J Ou Structural Control and Health Monitoring 22 (3), 433-448, 2015 | 79 | 2015 |
| Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach Y Bao, Z Tang, H Li Structural Health Monitoring 19 (1), 293-304, 2020 | 70 | 2020 |
| Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach Z Chen, H Li, Y Bao Structural Health Monitoring 18 (4), 1168-1188, 2019 | 67 | 2019 |
