Generalized deep neural network for seismic site response prediction with transfer learning

Lin Li, Feng Jin, Duruo Huang, Chunhui He, Fulong Ma,
Generalized deep neural network for seismic site response prediction with transfer learning,
Engineering Applications of Artificial Intelligence,
Volume 158, Part A,
2025,
111546,
ISSN 0952-1976,
https://doi.org/10.1016/j.engappai.2025.111546.
(https://www.sciencedirect.com/science/article/pii/S0952197625015489)
Abstract: Accurate prediction of site-specific seismic responses plays a pivotal role in evaluating earthquake effects on infrastructure. Traditional physics-based methods suffer from inherent model assumptions, significant parameter uncertainty, and high computational costs. This study proposes a generalized deep neural network that integrates seismic motion data and site information to predict three-directional seismic responses across various site types. Trained on an extensive dataset of recorded data from Kiban Kyoshin Network in Japan, the model demonstrated excellent performance on the test set, with correlation coefficients reaching 97 % between the predicted and target results. Utilizing transfer learning techniques, it was adapted to seismic response prediction at new sites not included in the training set. Compared to the state-of-the-art finite element method, the retrained model significantly improved prediction accuracy, with an overall average error reduction of approximately 50 %. Additionally, the model effectively captured the nonlinear response characteristics of a site during strong seismic events without any strong motion data to retrain. The proposed model demonstrated superior prediction accuracy, higher computational efficiency, and stronger generalization capabilities compared to traditional physics-based models.
Keywords: Seismic site response; Deep learning; Transfer learning; Kiban Kyoshin network