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Data‐Physical Fusion Deep Learning for Site Seismic Response Using KiK‐Net Records
In the realm of earthquake engineering, response spectra play a crucial role in characterizing the effects of site dynamic characteristics under seismic activity. Consequently, accurately predicting seismic response spectra is of paramount importance. We have developed a physics‐guided bidirectional long short‐term memory neural network model (Phy‐BiLSTM) that is proficient in predicting site seismic response based on bedrock records. The core principle of the Phy‐BiLSTM is to improve the alignment between the solution space and the ground truth by integrating physics knowledge obtained from the physical model. The model introduced in this study utilized the 5%‐damped response spectra, which were derived from strong ground motion records collected at the KiK‐net downhole array. The results substantiate the performance enhancement of Phy‐BiLSTM in comparison to the data‐driven BiLSTM model. Furthermore, we conduct a comparative analysis of the Phy‐BiLSTM model against traditional methods (EQ, SBSR) as well as other neural network architectures (CNN and LSTM). The result highlights the advantages of Phy‐BiLSTM in accurately predicting the site seismic response.
Data‐Physical Fusion Deep Learning for Site Seismic Response Using KiK‐Net Records
In the realm of earthquake engineering, response spectra play a crucial role in characterizing the effects of site dynamic characteristics under seismic activity. Consequently, accurately predicting seismic response spectra is of paramount importance. We have developed a physics‐guided bidirectional long short‐term memory neural network model (Phy‐BiLSTM) that is proficient in predicting site seismic response based on bedrock records. The core principle of the Phy‐BiLSTM is to improve the alignment between the solution space and the ground truth by integrating physics knowledge obtained from the physical model. The model introduced in this study utilized the 5%‐damped response spectra, which were derived from strong ground motion records collected at the KiK‐net downhole array. The results substantiate the performance enhancement of Phy‐BiLSTM in comparison to the data‐driven BiLSTM model. Furthermore, we conduct a comparative analysis of the Phy‐BiLSTM model against traditional methods (EQ, SBSR) as well as other neural network architectures (CNN and LSTM). The result highlights the advantages of Phy‐BiLSTM in accurately predicting the site seismic response.
Data‐Physical Fusion Deep Learning for Site Seismic Response Using KiK‐Net Records
Chen, Su (author) / Hu, Xiaohu (author) / Jiang, Weiping (author) / Wang, Suyang (author) / Chen, Xingye (author) / Li, Xiaojun (author)
Earthquake Engineering & Structural Dynamics ; 54 ; 993-1008
2025-03-01
16 pages
Article (Journal)
Electronic Resource
English
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