| Structural damage identification based on autoencoder neural networks and deep learning CSN Pathirage, J Li, L Li, H Hao, W Liu, P Ni Engineering structures 172, 13-28, 2018 | 340 | 2018 |
| Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning L Huang, J Li, H Hao, X Li Tunnelling and Underground Space Technology 81, 265-276, 2018 | 146 | 2018 |
| Substructure damage identification based on response reconstruction in frequency domain and model updating J Li, SS Law, Y Ding Engineering structures 41, 270-284, 2012 | 140 | 2012 |
| Structural response reconstruction with transmissibility concept in frequency domain SS Law, J Li, Y Ding Mechanical Systems and Signal Processing 25 (3), 952-968, 2011 | 129 | 2011 |
| Lost data recovery for structural health monitoring based on convolutional neural networks G Fan, J Li, H Hao Structural Control and Health Monitoring 26 (10), e2433, 2019 | 124 | 2019 |
| Development and application of a deep learning–based sparse autoencoder framework for structural damage identification CSN Pathirage, J Li, L Li, H Hao, W Liu, R Wang Structural Health Monitoring 18 (1), 103-122, 2019 | 121 | 2019 |
| Vibration signal denoising for structural health monitoring by residual convolutional neural networks G Fan, J Li, H Hao Measurement 157, 107651, 2020 | 109 | 2020 |
| Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network KH Padil, N Bakhary, M Abdulkareem, J Li, H Hao Journal of Sound and vibration 467, 115069, 2020 | 100 | 2020 |
| Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference Z Ding, J Li, H Hao Mechanical Systems and Signal Processing 132, 211-231, 2019 | 92 | 2019 |
| Time‐varying system identification using variational mode decomposition P Ni, J Li, H Hao, Y Xia, X Wang, JM Lee, KH Jung Structural Control and Health Monitoring 25 (6), e2175, 2018 | 86 | 2018 |
| Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements W Zhang, J Li, H Hao, H Ma Mechanical Systems and Signal Processing 87, 410-425, 2017 | 86 | 2017 |
| Improved damage identification in bridge structures subject to moving loads: numerical and experimental studies J Li, SS Law, H Hao International Journal of Mechanical Sciences 74, 99-111, 2013 | 86 | 2013 |
| Development and application of a relative displacement sensor for structural health monitoring of composite bridges J Li, H Hao, K Fan, J Brownjohn Structural Control and Health Monitoring 22 (4), 726-742, 2015 | 85 | 2015 |
| Substructural response reconstruction in wavelet domain J Li, SS Law Journal of Applied Mechanics ASME 78 (4), 041010, 2011 | 85 | 2011 |
| Data driven structural dynamic response reconstruction using segment based generative adversarial networks G Fan, J Li, H Hao, Y Xin Engineering Structures 234, 111970, 2021 | 84 | 2021 |
| Towards next generation design of sustainable, durable, multi-hazard resistant, resilient, and smart civil engineering structures H Hao, K Bi, W Chen, TM Pham, J Li Engineering Structures 277, 115477, 2023 | 82 | 2023 |
| Damage identification of a target substructure with moving load excitation J Li, SS Law Mechanical Systems and Signal Processing 30, 78-90, 2012 | 79 | 2012 |
| Fatigue reliability evaluation of deck-to-rib welded joints in OSD considering stochastic traffic load and welding residual stress C Cui, Q Zhang, Y Luo, H Hao, J Li International journal of fatigue 111, 151-160, 2018 | 77 | 2018 |
| Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks G Fan, J Li, H Hao Structural Health Monitoring 20 (4), 1373-1391, 2021 | 73 | 2021 |
| Reliability analysis and design optimization of nonlinear structures P Ni, J Li, H Hao, W Yan, X Du, H Zhou Reliability engineering & system safety 198, 106860, 2020 | 73 | 2020 |
Hao HongGuangzhou University / Curtin University在 curtin.edu.au 的电子邮件经过验证
