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代表性论文 Liao, Y., Zhang, R.*, Wu, G., and Sun, H. (2023). “A Frequency-Based Ground Motion Clustering Approach for Data-Driven Surrogate Modeling of Bridges.” Journal of Engineering Mechanics, 149 (9), 04023069.
Liao, Y., Lin, R., Zhang, R.*, and Wu, G. (2023). “Attention-based LSTM (AttLSTM) neural network for seismic response modeling of bridges.” Computers & Structures, 275, 106915. Chen, Y., Sun, Z.*, Zhang, R., Yao, L., and Wu, G. (2023). “Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis.” Computers & Structures, 281, 107038. Liao, Y., Zhang, R., Lin, R., Zong, Z., and Wu, G.* (2022). A stacked residual LSTM network for nonlinear seismic response prediction of bridges. Engineering Mechanics,39, 1-12. Keivan, A., Zhang, R., Keivan, D., Phillips, B. M.*, Ikenaga, M., and Ikago, K. (2022). “Rate-independent linear damping for the improved seismic performance of inter-story isolated structures.” Journal of Earthquake Engineering, 26(2), 793-816. Zhang, R., Meng, L., Mao, Z., and Sun, H.* (2021). “Spatiotemporal deep learning for bridge response forecasting.” Journal of Structural Engineering, 147(6), 04021070. Zhang, R., Liu, Y., and Sun, H.* (2020). “Physics-informed multi-LSTM networks for metamodeling of nonlinear structures.” Computer Methods in Applied Mechanics and Engineering, 369, 113226. Zhang, R., Liu, Y., and Sun, H.* (2020). “Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling.” Engineering Structures, 215, 110704. Zhang, R., Hajjar, J., Sun, H.* (2020). “Machine learning approach for sequence clustering with applications to ground motion selection.” Journal of Engineering Mechanics, 146(6), 04020040. Chen, Z., Zhang, R., Zheng, J., and Sun, H.* (2020). “Sparse Bayesian learning for structural damage identification.” Mechanical Systems and Signal Processing, 140, 106689. Wu, J., Zhang, R., and Phillips, B.M.* (2020). “Structural seismic resilience evaluation through real-time hybrid simulation with online learning neural networks.” International Journal of Lifecycle Performance Engineering, 4 (1-3), 184-214. Zhang, R., Chen, Z., Chen, S., Zheng, J., Buyukozturk, O., and Sun, H.* (2019). “Deep long short-term memory networks for nonlinear structural seismic response prediction.” Computers and Structures, 220, 55-68. Zhang, R., and Phillips, B.M.* (2019). “Cyber-physical approach to the optimization of semiactive structural control under multiple earthquake ground motions.” Computer-Aided Civil and Infrastructure Engineering, 34(5), 402-414. Zhang, R., Phillips, B.M.*, Fernández Cabán P.L., and Masters, F.J. (2019). “Cyber-physical structural optimization using real-time hybrid simulation.” Engineering Structures, 195, 113-124. Zhang, R., and Phillips, B.M.* (2017). “Artificial specimen damping for substructure real-time hybrid simulation.” Journal of Engineering Mechanics, 143(8), 04017052. Zhang, R., Phillips, B.M.*, Taniguchi, S., Ikenaga, M., and Ikago, K. (2017). “Shake table real-time hybrid simulation techniques for the performance evaluation of buildings with inter-story isolation.” Structural Control and Health Monitoring, 24(10), e1971. Zhang, R., Lauenstein, P.V., and Phillips, B.M.* (2016). “Real-time hybrid simulation of a shear building with a uni-axial shake table.” Engineering Structures, 119, 217-229. Zhang, R., and Phillips, B.M.* (2016). “Performance and protection of base-isolated structures under blast loading.” Journal of Engineering Mechanics, 142(1), p. 04015063.
专著 Sensor Technologies for Civil Infrastructures. Editor: Wang, M. L., Lynch, J. P., & Sohn, H.,Woodhead Publishing, ISBN: 9780081026960. Chapter 18: Deep learning and data analytics for assessing seismic performance of civil infrastructures.
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