Machine learning-based estimation of the out-of-plane displacement of brick infill exposed to earthquake shaking

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dc.authorscopusid56884524300
dc.authorscopusid23480721400
dc.contributor.authorOnat O.
dc.contributor.authorTanyıldızı H.
dc.date.accessioned2024-08-04T19:59:37Z
dc.date.available2024-08-04T19:59:37Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study aims to develop machine learning-based prediction models for the out-of-plane displacement of infill walls under earthquake excitation by using machine learning methods and comparing them to select the best prediction. For this purpose, two shake table experiments are selected. A set of data is compiled from the selected shake table experiments that were conducted on unreinforced brick infill (URB) and bed joint reinforced infill (BJR) walls enclosed in a reinforced concrete frame (RCF). Then, machine learning models such as extreme learning machine (ELM), support vector regression (SVR), decision tree (DT), bootstrap aggregation ensemble (Bagging), and least-squares boosting ensemble (LSBoost) algorithms, were devised to estimate out-of-plane (OOP) displacements of URB and BJR. Also, the model performances were compared to each other's. The OOP displacements of URB were predicted with 0.995%, 0.983%, 0.989%, 0.994%, and 0.996% accuracy using ELM, SVR, DT, Bagging, and LSBoost, respectively. Furthermore, the ELM, SVR, DT, Bagging, and LSBoost methods predicted the OOP displacement of BJR with 0.983%, 0.568%, 0.623%, 0.606%, and 0.644% accuracy, respectively. This study found that the ELM method predicted OOP displacements of the BJR with higher accuracy than other methods. However, the LSBoost method demonstrated superior performance in estimating URB's OOP displacement. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.engappai.2024.109007
dc.identifier.issn0952-1976
dc.identifier.scopus2-s2.0-85198920449en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2024.109007
dc.identifier.urihttps://hdl.handle.net/11616/90746
dc.identifier.volume136en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBootstrap aggregation ensembleen_US
dc.subjectDecision treeen_US
dc.subjectExtreme learning machineen_US
dc.subjectLeast-squares boosting ensembleen_US
dc.subjectOut-of-plane displacement of infill wallen_US
dc.subjectSupport vector regressionen_US
dc.titleMachine learning-based estimation of the out-of-plane displacement of brick infill exposed to earthquake shakingen_US
dc.typeArticleen_US

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