Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114840
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorLuo, L-
dc.creatorSun, L-
dc.creatorLi, Y-
dc.creatorXia, Y-
dc.date.accessioned2025-09-01T01:52:48Z-
dc.date.available2025-09-01T01:52:48Z-
dc.identifier.issn0924-090X-
dc.identifier.urihttp://hdl.handle.net/10397/114840-
dc.language.isoenen_US
dc.publisherSpringer Dordrechten_US
dc.rights© The Author(s) 2024en_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Luo, L., Sun, L., Li, Y. et al. Structural nonlinear boundary condition identification using a hybrid physics data-driven approach. Nonlinear Dyn 113, 9605–9623 (2025) is available at https://doi.org/10.1007/s11071-024-10614-x.en_US
dc.subjectBlind nonlinearity identificationen_US
dc.subjectFinite element methoden_US
dc.subjectNonlinear boundary conditionen_US
dc.subjectPhysics-informed neural networken_US
dc.subjectStabilized central difference methoden_US
dc.titleStructural nonlinear boundary condition identification using a hybrid physics data-driven approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage9605-
dc.identifier.epage9623-
dc.identifier.volume113-
dc.identifier.issue9-
dc.identifier.doi10.1007/s11071-024-10614-x-
dcterms.abstractAs civil infrastructures often exhibit nonlinearities, the identification of nonlinear behaviors is crucial to assess the structural safety state. However, existing physics-driven methods can only estimate the nonlinear parameters given a known nonlinear behavior pattern. By contrast, the data-driven methods can merely map the load-response relationship at the structural level, rather than identify an accurate nonlinear mapping relationship at the component level. To address these issues, a hybrid physics-data-driven strategy is developed in this study to identify the blind nonlinearity. The nonlinear structural components are surrogated by a data-driven multilayer perceptron, and the linear ones are simulated by using the finite element method. Subsequently, the global stiffness matrix and restoring force vector are assembled according to the elemental topology relationship to obtain the hybrid model. The discrepancy between the measured and hybrid model-predicted responses is formulated as the loss function, by minimizing which of the MLPs are indirectly trained and the nonlinearities can be identified without knowing the nonlinearity type. Three numerical cases are used to verify the proposed method in identifying the elastic, hysteretic, and multiple nonlinear boundary conditions. Results show that the proposed method is robust given different noise levels, sensor placements, and nonlinear types. Moreover, the trained hybrid model possesses a strong generalization ability to accurately predict full-field structural responses.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationNonlinear dynamics, May 2025, v. 113, no. 9, p. 9605-9623-
dcterms.isPartOfNonlinear dynamics-
dc.identifier.scopus2-s2.0-105001085654-
dc.identifier.eissn1573-269X-
dc.description.validate202509 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TAen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis paper is supported by the National Natural Science Foundation of China (52378187) and the Technology Cooperation Project of Shanghai Qizhi Institute (SYXF0120020109).en_US
dc.description.pubStatusPublisheden_US
dc.description.TASpringer Nature (2024)en_US
dc.description.oaCategoryTAen_US
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