نمایش مختصر رکورد

dc.contributor.authorJoseph, Jikhilen_US
dc.contributor.authorSwalih C K, Ahmaden_US
dc.date.accessioned1402-08-26T19:39:02Zfa_IR
dc.date.accessioned2023-11-17T19:39:03Z
dc.date.available1402-08-26T19:39:02Zfa_IR
dc.date.available2023-11-17T19:39:03Z
dc.date.issued2023-09-01en_US
dc.date.issued1402-06-10fa_IR
dc.date.submitted2023-04-03en_US
dc.date.submitted1402-01-14fa_IR
dc.identifier.citationJoseph, Jikhil, Swalih C K, Ahmad. (2023). Implementation of Machine Learning in Structural Reliability Analysis. Journal of civil Engineering and Materials Application, 7(3)doi: 10.22034/jcema.2023.396301.1108en_US
dc.identifier.issn2676-232X
dc.identifier.issn2588-2880
dc.identifier.urihttps://dx.doi.org/10.22034/jcema.2023.396301.1108
dc.identifier.urihttps://www.jcema.com/article_179691.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/1047570
dc.description.abstractReliability is a probabilistic measure of structural safety. In Structural Reliability Analysis (SRA), both loads and resistances are modelled as probabilistic variables, and the failure of structure occurs when the total applied load is larger than the total resistance of the structure. The probability distribution of the loads as well as the resistance can depend upon multiple variables. Considering all these factors, the probability of failure of a structure is calculated.SRA can be used for systematic adjustment of structural safety factors, and for the probabilistic design and operation of structures. For example, SRA can be used to design a structure to operate during the desired lifetime safely, or it can be used for maintenance scheduling of structural systems to prevent potential failures. Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws inferences from a sample, while machine learning finds generalizable predictive patterns. ML methods can be applied to analytical and numerical SRA methods, such as First/Second-Order Reliability Methods (FORM/SORM) and First Order Second Moment (FOSM)en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherPenPuben_US
dc.relation.ispartofJournal of civil Engineering and Materials Applicationen_US
dc.relation.isversionofhttps://dx.doi.org/10.22034/jcema.2023.396301.1108
dc.subjectprobabilistic modelsen_US
dc.subjectStructural reliability analysisen_US
dc.subjectMachine Learningen_US
dc.subjectStructural Engineeringen_US
dc.titleImplementation of Machine Learning in Structural Reliability Analysisen_US
dc.typeTexten_US
dc.typeMini Reviewen_US
dc.contributor.departmentDept of Civil Engineering , Govt Engineering College Thrissuren_US
dc.contributor.departmentDept of Civil Engineering Govt Engineering College Thrissuren_US
dc.citation.volume7
dc.citation.issue3
nlai.contributor.orcid0000-0001-5436-9653


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