首页|Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

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High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based real-time structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story rein-forced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave ampli-tudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

Structural seismic response predictionPhysics information informedReal-time predictionEarthquake engineeringData-driven machine learning

Ying Zhou、Shiqiao Meng、Yujie Lou、Qingzhao Kong

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State Key Laboratory of Disaster Reduction in Civil Engineering,Tongji University,Shanghai 200092,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaXPLORER PRIZE of New Cornerstone Science FoundationShanghai Social Development Science and Technology Research ProjectShanghai Urban Digital Transformation Special Fund

52025083U213920922dz1201400202201033

2024

10.1016/j.eng.2023.08.011

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.35(4)