Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97371
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorGuo, HYen_US
dc.creatorDong, Yen_US
dc.creatorGardoni, Pen_US
dc.creatorGu, XLen_US
dc.date.accessioned2023-03-06T01:17:50Z-
dc.date.available2023-03-06T01:17:50Z-
dc.identifier.urihttp://hdl.handle.net/10397/97371-
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineersen_US
dc.rights© 2021 American Society of Civil Engineers.en_US
dc.rightsThis material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/AJRUA6.0001153.en_US
dc.subjectEquivalent extreme performance functionen_US
dc.subjectMaintenanceen_US
dc.subjectMultiple deteriorationsen_US
dc.subjectPoint-evolution kernel density estimation (PKDE)en_US
dc.subjectTime-dependent reliabilityen_US
dc.titleTime-dependent reliability analysis based on point-evolution kernel density estimation : comprehensive approach with continuous and shock deterioration and maintenanceen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationTitle on author’s file: Point-Evolution Kernel Density Estimation based Time-Dependent Reliability Analysis: A Comprehensive Approach with Continuous and Shock Deteriorations and Maintenanceen_US
dc.identifier.volume7en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1061/AJRUA6.0001153en_US
dcterms.abstractCivil infrastructure may degrade due to the adverse effects of continuous damage (e.g., reinforcement corrosion) and sudden shocks (e.g., earthquakes) during its service life. Many studies have been conducted in the field of reliability-informed life-cycle assessment, but there is still a need for a general and efficient method to assess the time-dependent performance of aging structures by considering different deterioration scenarios and maintenance actions in a unified manner. Some of the traditional methods may have difficulties in handling multiple deteriorations, nonlinear models, a large number of uncertainties, scenarios of nondifferentiable performance functions, and combined effects of deterioration and maintenance. This paper develops a novel approach for a time-dependent reliability analysis based on the proposed point-evolution kernel density estimation (PKDE) method and equivalent extreme performance function. The proposed approach allows consideration of various uncertainties (e.g., external loads, deterioration scenarios, and maintenance models) and the associated correlation effects. In the proposed approach, both the progressive deterioration and sudden damages are considered in the modeling of the performance function. Besides, different types of maintenance schemes are assessed. The equivalent performance function is established, and the proposed PKDE method is used to address the first passage problem and nondifferentiable performance function within a time-dependent reliability analysis. An illustrative example is made to demonstrate the feasibility and accuracy of the proposed PKDE method. The computational results using the proposed method are verified by comparison with those from Monte Carlo simulations (MCS).en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationASCE - ASME journal of risk and uncertainty in engineering systems, Part A: civil engineering, Sept. 2021, v. 7, no. 3, 04021032en_US
dcterms.isPartOfASCE - ASME journal of risk and uncertainty in engineering systems, Part A: civil engineeringen_US
dcterms.issued2021-09-
dc.identifier.scopus2-s2.0-85108028347-
dc.identifier.eissn2376-7642en_US
dc.identifier.artn04021032en_US
dc.description.validate202203 bcfcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCEE-0185-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Key R&D Program of China; NNSFCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS52720571-
dc.description.oaCategoryGreen (AAM)en_US
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