Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Y Fei, W Liao, P Zhao, X Lu… - Earthquake Engineering & …, 2024 - Wiley Online Library
To address the issue of costly computational expenditure related to high‐fidelity numerical
models, surrogate models have been widely used in various engineering tasks, including …

Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning

C Zhang, W Wen, C Zhai, J Jia, B Zhou - Engineering Structures, 2024 - Elsevier
Efficiently predicting the seismic response of urban building clusters is essential for
preemptively identifying potential seismic hazards prior to an earthquake and optimizing …

Seismic response prediction of a damped structure based on data-driven machine learning methods

T Zhang, W Xu, S Wang, D Du, J Tang - Engineering Structures, 2024 - Elsevier
Dam** technology has been widely used because of its good vibration control effect.
However, due to the strong nonlinearity of the added dampers, accurately predicting the …

Multimodal fusion hybrid neural network approach for multi-class damage classification in high-speed rail track-bridge systems with multi-parameter

K Peng, W Zhou, L Jiang, L **ong, WJ Yan - Engineering Structures, 2025 - Elsevier
Widespread attention has been devoted to assessing the seismic performance of high-
speed railway (HSR) networks in regions vulnerable to earthquakes. Existing structural …

Deep Learning in Earthquake Engineering: A Comprehensive Review

Y **e - arxiv preprint arxiv:2405.09021, 2024 - arxiv.org
This article surveys the growing interest in utilizing Deep Learning (DL) as a powerful tool to
address challenging problems in earthquake engineering. Despite decades of advancement …