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Modelling and stochastic updating of nonlinear structural joints

Pushpa Pandey, Nidhal Jamia Orcid Logo, Tanmoy Chatterjee Orcid Logo, Hamed Haddad Khodaparast Orcid Logo, Michael Friswell

Mechanical Systems and Signal Processing, Volume: 232, Start page: 112697

Swansea University Authors: Pushpa Pandey, Nidhal Jamia Orcid Logo, Hamed Haddad Khodaparast Orcid Logo, Michael Friswell

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DOI (Published version): 10.1016/j.ymssp.2025.112697

Abstract

This paper presents a novel framework for modelling and stochastic updating of nonlinear systems in structural dynamics, with particular emphasis on joint structures. The key innovation lies in the integration of experimental data, system identification, and probabilistic methods to develop a compre...

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Published in: Mechanical Systems and Signal Processing
ISSN: 0888-3270
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69328
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The key innovation lies in the integration of experimental data, system identification, and probabilistic methods to develop a comprehensive understanding of nonlinear dynamic behaviour. The methodology employs a specialised control system to extract backbone curves, which characterise the system&#x2019;s fundamental nonlinear response. A data-driven approach is implemented to identify the dominant system dynamics, which is then seamlessly integrated with the control system to create an analytical model. A significant contribution of this work is the development of a stochastic framework that combines the analytical model with measured system responses through probabilistic sampling methods, enabling robust uncertainty quantification. To address the computational challenges inherent in such complex simulations, the framework incorporates a deep learning model trained on both experimental and analytical data. This integration substantially improves computational efficiency while maintaining accuracy in predicting nonlinear dynamic responses. The framework&#x2019;s effectiveness is demonstrated through application to jointed structures, where traditional deterministic approaches often fall short. By providing a probabilistic perspective on system behaviour, this methodology offers more reliable predictions of dynamic responses under varying conditions. 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spelling 2025-04-23T11:27:48.8217051 v2 69328 2025-04-23 Modelling and stochastic updating of nonlinear structural joints c5b9c91974d44b88920ef13e914a9bdd Pushpa Pandey Pushpa Pandey true false 846b2cd3a7717b296654010df30cb22a 0000-0003-0643-7812 Nidhal Jamia Nidhal Jamia true false f207b17edda9c4c3ea074cbb7555efc1 0000-0002-3721-4980 Hamed Haddad Khodaparast Hamed Haddad Khodaparast true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2025-04-23 This paper presents a novel framework for modelling and stochastic updating of nonlinear systems in structural dynamics, with particular emphasis on joint structures. The key innovation lies in the integration of experimental data, system identification, and probabilistic methods to develop a comprehensive understanding of nonlinear dynamic behaviour. The methodology employs a specialised control system to extract backbone curves, which characterise the system’s fundamental nonlinear response. A data-driven approach is implemented to identify the dominant system dynamics, which is then seamlessly integrated with the control system to create an analytical model. A significant contribution of this work is the development of a stochastic framework that combines the analytical model with measured system responses through probabilistic sampling methods, enabling robust uncertainty quantification. To address the computational challenges inherent in such complex simulations, the framework incorporates a deep learning model trained on both experimental and analytical data. This integration substantially improves computational efficiency while maintaining accuracy in predicting nonlinear dynamic responses. The framework’s effectiveness is demonstrated through application to jointed structures, where traditional deterministic approaches often fall short. By providing a probabilistic perspective on system behaviour, this methodology offers more reliable predictions of dynamic responses under varying conditions. The successful implementation of this approach represents a significant advancement in the field of structural dynamics, particularly for complex systems where uncertainty quantification is crucial for accurate response prediction. Journal Article Mechanical Systems and Signal Processing 232 112697 Elsevier BV 0888-3270 Likelihood function; Backbone curves; Stochastic nonlinear dynamics; Bayesian inference; Markov Chain Monte Carlo; Phase-locked loop; Deep learning 1 6 2025 2025-06-01 10.1016/j.ymssp.2025.112697 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors acknowledge the support of the Engineering and Physical Sciences Research Council, United Kingdom through the award of the Programme Grant “Digital Twins for Improved Dynamic Design”, grant number EP/R006768/1 and Tribomechadynamics Research Challenge for providing the experimental data. P. Pandey and Hamed Haddad Khodaparast acknowledge the funding from the RCUK Energy Programme [grant number EP/T012250/1]. 2025-04-23T11:27:48.8217051 2025-04-23T11:22:41.3546933 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Pushpa Pandey 1 Nidhal Jamia 0000-0003-0643-7812 2 Tanmoy Chatterjee 0000-0002-9374-7992 3 Hamed Haddad Khodaparast 0000-0002-3721-4980 4 Michael Friswell 5 69328__34063__9a11292be58b4be4953455426d80f86c.pdf 69328.VoR.pdf 2025-04-23T11:26:03.0445572 Output 5276343 application/pdf Version of Record true © 2025 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Modelling and stochastic updating of nonlinear structural joints
spellingShingle Modelling and stochastic updating of nonlinear structural joints
Pushpa Pandey
Nidhal Jamia
Hamed Haddad Khodaparast
Michael Friswell
title_short Modelling and stochastic updating of nonlinear structural joints
title_full Modelling and stochastic updating of nonlinear structural joints
title_fullStr Modelling and stochastic updating of nonlinear structural joints
title_full_unstemmed Modelling and stochastic updating of nonlinear structural joints
title_sort Modelling and stochastic updating of nonlinear structural joints
author_id_str_mv c5b9c91974d44b88920ef13e914a9bdd
846b2cd3a7717b296654010df30cb22a
f207b17edda9c4c3ea074cbb7555efc1
5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv c5b9c91974d44b88920ef13e914a9bdd_***_Pushpa Pandey
846b2cd3a7717b296654010df30cb22a_***_Nidhal Jamia
f207b17edda9c4c3ea074cbb7555efc1_***_Hamed Haddad Khodaparast
5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
author Pushpa Pandey
Nidhal Jamia
Hamed Haddad Khodaparast
Michael Friswell
author2 Pushpa Pandey
Nidhal Jamia
Tanmoy Chatterjee
Hamed Haddad Khodaparast
Michael Friswell
format Journal article
container_title Mechanical Systems and Signal Processing
container_volume 232
container_start_page 112697
publishDate 2025
institution Swansea University
issn 0888-3270
doi_str_mv 10.1016/j.ymssp.2025.112697
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 - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description This paper presents a novel framework for modelling and stochastic updating of nonlinear systems in structural dynamics, with particular emphasis on joint structures. The key innovation lies in the integration of experimental data, system identification, and probabilistic methods to develop a comprehensive understanding of nonlinear dynamic behaviour. The methodology employs a specialised control system to extract backbone curves, which characterise the system’s fundamental nonlinear response. A data-driven approach is implemented to identify the dominant system dynamics, which is then seamlessly integrated with the control system to create an analytical model. A significant contribution of this work is the development of a stochastic framework that combines the analytical model with measured system responses through probabilistic sampling methods, enabling robust uncertainty quantification. To address the computational challenges inherent in such complex simulations, the framework incorporates a deep learning model trained on both experimental and analytical data. This integration substantially improves computational efficiency while maintaining accuracy in predicting nonlinear dynamic responses. The framework’s effectiveness is demonstrated through application to jointed structures, where traditional deterministic approaches often fall short. By providing a probabilistic perspective on system behaviour, this methodology offers more reliable predictions of dynamic responses under varying conditions. The successful implementation of this approach represents a significant advancement in the field of structural dynamics, particularly for complex systems where uncertainty quantification is crucial for accurate response prediction.
published_date 2025-06-01T05:27:53Z
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score 11.444327

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