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72 results on '"Sun, Zeyang"'

67. Loading‐Induced Reduction in Sclerostin as a Mechanism of Subchondral Bone Plate Sclerosis in Mouse Knee Joints During Late‐Stage Osteoarthritis

Author

Jia, Haoruo, primary, Ma, Xiaoyuan, additional, Wei, Yulong, additional, Tong, Wei, additional, Tower, Robert J., additional, Chandra, Abhishek, additional, Wang, Luqiang, additional, Sun, Zeyang, additional, Yang, Zhaochun, additional, Badar, Farid, additional, Zhang, Kairui, additional, Tseng, Wei‐Ju, additional, Kramer, Ina, additional, Kneissel, Michaela, additional, Xia, Yang, additional, Liu, X. Sherry, additional, Wang, James H. C., additional, Han, Lin, additional, Enomoto‐Iwamoto, Motomi, additional, and Qin, Ling, additional

Published
2017
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71. EGFR signalling is critical for maintaining the superficial layer of articular cartilage and preventing osteoarthritis progression

Author

Jia, Haoruo, primary, Ma, Xiaoyuan, additional, Doyran, Basak, additional, Tong, Wei, additional, Sun, Zeyang, additional, Yang, Zhaochun, additional, Zhang, Xianrong, additional, Chandra, Abhishek, additional, Wang, James, additional, Han, Lin, additional, Enomoto-Iwamoto, Motomi, additional, and Qin, Ling, additional

Published
2016
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72. Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis.

Author

Chen, Youjun, Sun, Zeyang, Zhang, Ruiyang, Yao, Liuzhen, and Wu, Gang

Subjects
*EARTHQUAKE damage, *DISASTER resilience, *CONVOLUTIONAL neural networks, *STRUCTURAL health monitoring, *RECURRENT neural networks, *SEISMIC response, *DEEP learning
Abstract

• Improved transformer network used in predicting nonlinear structural response and damage state. • A new fragility analysis method considering multiple damage indexes simultaneously. • Considering structural peak and residual damages in unison to estimate post-disaster resilience. • Quickly giving the assurance rate that the structure meets certain performance objectives. This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. [ABSTRACT FROM AUTHOR]

Published
2023
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