A Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

dc.authorid0000-0003-3064-7131
dc.contributor.authorIşık, Mehmet Fatih
dc.contributor.authorAvcil, Fatih
dc.contributor.authorHarirchian,Ehsan
dc.contributor.authorAkif Bülbül, Mehmet
dc.contributor.authorHadzima-Nyarko, Marijana
dc.contributor.authorIşık, Ercan
dc.contributor.authorİzol, Rabia
dc.contributor.authorRadu, Dorin
dc.date.accessioned2024-01-23T07:51:26Z
dc.date.available2024-01-23T07:51:26Z
dc.date.issued2023en_US
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractAbstract: The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.
dc.description.provenanceSubmitted by Zeynep Umut NARİN (umutarslan@hitit.edu.tr) on 2024-01-23T07:51:12Z No. of bitstreams: 1 mehmet-fatih-ısık2023.pdf: 5889836 bytes, checksum: 17efc21dc66170f6bcafbb29d6a1977d (MD5)en
dc.description.provenanceApproved for entry into archive by Zeynep Umut NARİN (umutarslan@hitit.edu.tr) on 2024-01-23T07:51:26Z (GMT) No. of bitstreams: 1 mehmet-fatih-ısık2023.pdf: 5889836 bytes, checksum: 17efc21dc66170f6bcafbb29d6a1977d (MD5)en
dc.description.provenanceMade available in DSpace on 2024-01-23T07:51:26Z (GMT). No. of bitstreams: 1 mehmet-fatih-ısık2023.pdf: 5889836 bytes, checksum: 17efc21dc66170f6bcafbb29d6a1977d (MD5) Previous issue date: 2023en
dc.identifier.citationIşık, M. F., Avcil, F., Harirchian, E., Bülbül, M. A., Hadzima-Nyarko, M., Işık, E., ... & Radu, D. (2023). A Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings. Sustainability, 15(12), 9715.
dc.identifier.doi10.3390/su15129715
dc.identifier.issn2071-1050
dc.identifier.issue12en_US
dc.identifier.urihttps://doi.org/10.3390/su15129715
dc.identifier.urihttps://hdl.handle.net/11491/8726
dc.identifier.volume15en_US
dc.identifier.wosWOS:001015855700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorIşık, Mehmet Fatih
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMid-riseen_US
dc.subjectRegular RC buildingen_US
dc.subjectTarget displacementen_US
dc.subjectANNen_US
dc.subjectOptimization algorithmen_US
dc.titleA Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
dc.typeArticle

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