Prediction of seismic response of building structures using a CNN-LSTM-ATT network with transfer learning
Yongxi Shen, Gao Ma, Hyeon‐Jong Hwang, Dae Jin Kim, Zhenhao Zhang
Abstract
Accurate prediction of the seismic response of buildings is crucial for their structural assessment and performance evaluation. To this end, leveraging recent advancements in deep learning, this study introduces a convolutional long short-term memory neural network with attention mechanism (CNN-LSTM-ATT) for predicting the seismic response of moment frame and shear wall-frame structures. Through ablation experiments, the effectiveness of the convolutional and attention blocks was validated. Furthermore, employing transfer learning, the CNN-LSTM-ATT model was fine-tuned to predict seismic response across different target buildings. Two distinct transfer learning scenarios were investigated: 1) transfer from finite element models with various parameters of the same structure; and 2) transfer from finite element models to same actual structures. These scenarios demonstrate that model-based transfer learning significantly enhances the prediction accuracy of CNN-LSTM-ATT. Compared to the finite element models, the model based on transfer learning (i.e., with fine-tuning) in various scenarios, accurately predicted the nonlinear behaviors of structures. Thus, the proposed method is applicable for easy modeling and rapid prediction of dynamic response in various building structures under earthquakes.