Attention-based LSTM (AttLSTM) neural network for Seismic Response Modeling of Bridges. (15th January 2023)

• Record Type: Journal Article
• Authors: Liao, Yuchen; Lin, Rong; Zhang, Ruiyang; Wu, Gang
• Is Part Of: Computers & structures. Volume 275(2023)
• Publication Date: 2023-01-15
• Subjects: Bridge engineering -- Deep learning -- Long short term memory neural network -- Attention mechanism -- Seismic response modelling; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2022.106915
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Developing a deep learning architecture to model seismic responses of bridges. Proposing attention mechanism to enhance model training and prediction performance. Outperforming traditional LSTM neural networks in response prediction. Validating the proposed methods through synthetic dataset and field measurements. Abstract: Accurate prediction of bridge responses plays an essential role in health monitoring and safety assessment of bridges subjected to dynamic loads such as earthquakes. To this end, this paper leverages the recent advances in deep learning and proposes an innovative attention-based recurrent neural network for metamodeling of bridge structures under seismic hazards. The key concept is to establish an attention-based long short-term memory neural network (AttLSTM) to learn the dynamics from limited training data and make predictions of bridge responses against unseen earthquakes. In particular, an attention mechanism is proposed to enhance the selection of more informative components among sequential data for better learning from limited data. The performance of the proposed AttLSTM neural network was validated through both numerical and real-world data of a girder bridge and a cable-stayed bridge to systematically evaluate the prediction performance of the proposed method. In addition, the classical LSTM neural network was selected as the baseline model to show the favorable performance of the proposed attention mechanism. Results indicate that the proposed method with attention mechanism outperforms the compared state-of-the-art LSTM in terms of both accuracy and reliability.