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Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction

Yongxin Wu, Zhanpeng Yin, Yufeng Gao, Shangchuan Yang, Yue Hou Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 443, Start page: 118079

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1016/j.cma.2025.118079

Abstract

Seismic response prediction presents a significant challenge in earthquake engineering, particularly in balancing computational efficiency with physical accuracy. Traditional numerical methods are computationally expensive for performing large-scale nonlinear analyses, while data-driven machine lear...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70719
first_indexed 2025-10-18T09:30:12Z
last_indexed 2025-12-05T18:10:23Z
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spelling 2025-12-04T10:11:27.8755736 v2 70719 2025-10-18 Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2025-10-18 ACEM Seismic response prediction presents a significant challenge in earthquake engineering, particularly in balancing computational efficiency with physical accuracy. Traditional numerical methods are computationally expensive for performing large-scale nonlinear analyses, while data-driven machine learning approaches, though computational efficiency, often lack physical constraints and sufficient training data. Physics-Informed Neural Networks (PINNs), an emerging approach that integrates physical laws with deep learning techniques to solve complex scientific and engineering problems, show great potential. However, incorporating nonlinear constitutive models to accurately describe the structural behavior under seismic loading remains a challenge. In this study, a new framework, constitutive model-constrained physics-informed neural networks (CM-PINNs), is proposed to address this issue. This framework enhances prediction accuracy and physical interpretability by incorporating nonlinear constitutive constraints into the loss function. It also uses a fully connected skip LSTM architecture and implements an adaptive loss weight initialization strategy. Numerical validation demonstrates the superior performance of the CM-PINNs framework in simulating single-degree-of-freedom nonlinear seismic responses. Under limited training data conditions, CM-PINNs demonstrates notably superior performance compared to existing methods such as physics-informed multi-LSTM networks (PhyLSTM). Additionally, the scalability of CM-PINNs is verified through its application to multi-layer shear building structures. The results demonstrate that CM-PINNs provide a computationally efficient and reliable approach for seismic response prediction. Journal Article Computer Methods in Applied Mechanics and Engineering 443 118079 Elsevier BV 0045-7825 Constitutive model; Physics-informed deep learning; Meta-modeling; Adaptive loss weight initialization; LSTMCM-PINNs 1 8 2025 2025-08-01 10.1016/j.cma.2025.118079 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University The authors would like to acknowledge the support of the National Natural Science Foundation of China under Grant No 42377140. 2025-12-04T10:11:27.8755736 2025-10-18T09:39:01.4937149 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yongxin Wu 1 Zhanpeng Yin 2 Yufeng Gao 3 Shangchuan Yang 4 Yue Hou 0000-0002-4334-2620 5
title Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
spellingShingle Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
Yue Hou
title_short Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
title_full Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
title_fullStr Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
title_full_unstemmed Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
title_sort Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Yongxin Wu
Zhanpeng Yin
Yufeng Gao
Shangchuan Yang
Yue Hou
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 443
container_start_page 118079
publishDate 2025
institution Swansea University
issn 0045-7825
doi_str_mv 10.1016/j.cma.2025.118079
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description Seismic response prediction presents a significant challenge in earthquake engineering, particularly in balancing computational efficiency with physical accuracy. Traditional numerical methods are computationally expensive for performing large-scale nonlinear analyses, while data-driven machine learning approaches, though computational efficiency, often lack physical constraints and sufficient training data. Physics-Informed Neural Networks (PINNs), an emerging approach that integrates physical laws with deep learning techniques to solve complex scientific and engineering problems, show great potential. However, incorporating nonlinear constitutive models to accurately describe the structural behavior under seismic loading remains a challenge. In this study, a new framework, constitutive model-constrained physics-informed neural networks (CM-PINNs), is proposed to address this issue. This framework enhances prediction accuracy and physical interpretability by incorporating nonlinear constitutive constraints into the loss function. It also uses a fully connected skip LSTM architecture and implements an adaptive loss weight initialization strategy. Numerical validation demonstrates the superior performance of the CM-PINNs framework in simulating single-degree-of-freedom nonlinear seismic responses. Under limited training data conditions, CM-PINNs demonstrates notably superior performance compared to existing methods such as physics-informed multi-LSTM networks (PhyLSTM). Additionally, the scalability of CM-PINNs is verified through its application to multi-layer shear building structures. The results demonstrate that CM-PINNs provide a computationally efficient and reliable approach for seismic response prediction.
published_date 2025-08-01T05:31:31Z
_version_ 1851098074939654144
score 11.089386

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