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1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. Zhang, R. , Meng, L., Mao, Z., and Sun, H.* (2021). “Spatiotemporal deep learning for bridge response forecasting.” Journal of Structural Engineering , 147(6), 04021070.
7. 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.
8. Zhang, R. , Liu, Y., and Sun, H.* (2020). “Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling.” Engineering Structures , 215, 1070704.
9. 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.
10. Chen, Z., Zhang, R. , Zheng, J., and Sun, H.* (2020). “Sparse Bayesian learning for structural damage identification.” Mechanical Systems and Signal Processing , 140, 106689.
11. 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.
12. 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.
13. 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.
14. 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.
15. Zhang, R. , and Phillips, B.M.* (2017). “Artificial specimen damping for substructure real-time hybrid simulation.” Journal of Engineering Mechanics , 143(8), 04017052.
16. 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.
17. 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.
18. 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.