Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116606
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorHu, Sen_US
dc.creatorZhu, Sen_US
dc.creatorAlam, Men_US
dc.creatorWang, Wen_US
dc.date.accessioned2026-01-06T02:09:13Z-
dc.date.available2026-01-06T02:09:13Z-
dc.identifier.isbn en_US
dc.identifier.issn0141-0296en_US
dc.identifier.urihttp://hdl.handle.net/10397/116606-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.rights© 2022 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Hu, S., Zhu, S., Shahria Alam, M., & Wang, W. (2022). Machine learning-aided peak and residual displacement-based design method for enhancing seismic performance of steel moment-resisting frames by installing self-centering braces. Engineering Structures, 271, 114935 is available at https://doi.org/10.1016/j.engstruct.2022.114935.en_US
dc.subjectMachine learningen_US
dc.subjectMoment-resisting framesen_US
dc.subjectPeak and residual displacementen_US
dc.subjectPost-earthquake repairabilityen_US
dc.subjectResidual displacement-based design methoden_US
dc.subjectSelf-centering braceen_US
dc.titleMachine learning-aided peak and residual displacement-based design method for enhancing seismic performance of steel moment-resisting frames by installing self-centering bracesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage en_US
dc.identifier.epage en_US
dc.identifier.volume271en_US
dc.identifier.issue en_US
dc.identifier.doi10.1016/j.engstruct.2022.114935en_US
dcterms.abstractConventional steel moment-resisting frames (SMRFs) absorb seismic energy through steel yielding behavior, leading to significant residual displacement. Although steel yielding behavior can ensure the seismic safety of SMRFs under strong earthquakes, excessive residual displacement may lead to post-earthquake demolition decisions, causing a large amount of economic loss. This paper aims to develop a peak and residual displacement-based design (PRDBD) method for controlling the peak and residual inter-story drift responses of SMRFs by installing self-centering braces. The peak and residual displacements are both set as the design targets in the proposed PRDBD method. To this end, the machine learning prediction models of inelastic and residual displacement ratios were first developed based on the median responses of single-degree-of-freedom systems under earthquakes. The detailed design steps of the proposed PRDBD method were subsequently introduced. The three- and nine-story demonstration buildings were retrofitted by using the PRDBD method with two different design targets. Static and dynamic analyses were conducted to validate the effectiveness of the proposed PRDBD method. The static analysis results indicated that the self-centering braces could efficiently enhance the SMRF’s stiffness and strength. The retrofitted SMRFs showed no strength deterioration, whereas the original SMRFs showed obvious strength deterioration at the roof drifts of 3.2% and 2.5% in the three- and nine-story buildings, respectively. The dynamic analysis results confirm that the self-centering braces can efficiently reduce the peak and residual inter-story drift responses of the existing SMRFs and the retrofitted SMRFs can achieve the peak and residual inter-story performance objectives under the considered seismic intensity. Moreover, the retrofitted SMRFs can be fully recoverable after maximum considered earthquakes by controlling the maximum residual inter-story drift lower than 0.2%.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering structures, 15 Nov. 2022, v. 271, 114935en_US
dcterms.isPartOfEngineering structuresen_US
dcterms.issued2022-11-15-
dc.identifier.scopus2-s2.0-85137631512-
dc.identifier.pmid -
dc.identifier.eissn1873-7323en_US
dc.identifier.artn114935en_US
dc.description.validate202601 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera4247-
dc.identifier.SubFormID52432-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe financial support from the Research Grants Council of Hong Kong (Grant Nos. PolyU 152246/18E, C7038-20G, T22-502/18-R), and the Hong Kong Polytechnic University (Grant Nos. ZE2L, ZVX6, and P0038795).en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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