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Wang-ji Yan

As seen in: Wiley Online Library, MDPI
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Articles

Transmissibility Physics‐Guided Deep Learning Network for Seismic Response Prediction Under Limited Domain Knowledge, Sparse Measurements, and Unknown Excitations

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By Yuntai Zhang, Wang-ji Yan, Ka-Veng Yuen, Li-Zhong Jiang
| Wiley Online Library Verified
Data Availability Statement Research data are not shared. References 1, , and , “Data-Driven Post-Earthquake Rapid Structural Safety Assessment,” Earthquake Engineering & Structural Dynamics 44 (2015): 549–562, https://doi.org/10.1002/eqe.2541. 2 and , “Rapid Evaluation of Structural Soundness of Steel Frames Using a Coupling Coefficient (CC)-Based Method,” Earthquake Engineering & Structural Dynamics 52 (2023): 1182–1204, https://doi.org/10.1002/eqe.3811.
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DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection

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By Wang-ji Yan
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Two‐stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification

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By Wen-Jing Zhang, Ka-Veng Yuen, Wang-ji Yan
| Wiley Online Library Verified
1 INTRODUCTION System identification is the process of constructing a mathematical model emulating the system behavior based on the observed data (Ljung, 1998; Zadeh, 1962). Various techniques are employed to model systems in civil and infrastructure engineering, including building structures (Amezquita-Sanchez et al., 2017; Li et al., 2017), offshore and marine structures (Pezeshki, Adeli, et al., 2023; Pezeshki, Pavlou, et al., 2023), and bridges (Shokravi et al., 2024; Xu et al., 2024).
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