prikaz prve stranice dokumenta A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
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Scientific paper - Original scientific paper
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 (2023), 2; 9715. https://doi.org/10.3390/su15129715
Işık, Mehmet Fatih; Avcil, Fatih; Harirchian, Ehsan; Bülbül, Mehmet Akif; Hadzima-Nyarko, Marijana; Işık, Ercan; İzol, Rabia; Radu, Dorin

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Işık, 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. (2). doi: 10.3390/su15129715

Işık, Mehmet Fatih, et al. "A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings." Sustainability, vol. 15, no. 2, 2023. https://doi.org/10.3390/su15129715

Işık, Mehmet Fatih, Fatih Avcil, Ehsan Harirchian, Mehmet Akif Bülbül, Marijana Hadzima Nyarko, Ercan Işık, Rabia İzol and Dorin Radu. "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, no. 2 (2023). https://doi.org/10.3390/su15129715

Işık, M. F., et al. (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(2). doi: 10.3390/su15129715

Işık MF, Avcil F, Harirchian E, Bülbül MA, Hadzima Nyarko M, Işık E, and sur.. A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings. Sustainability [Internet]. 2023 [cited 2025 January 12];15(2). doi: 10.3390/su15129715

M. F. Işık, et al., "A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings", Sustainability, vol. 15, no. 2, 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:272681. [Accessed: 12 January 2025]

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Metadata
Title (english)A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
AuthorMehmet Fatih Işık
AuthorFatih Avcil
AuthorEhsan Harirchian
AuthorMehmet Akif Bülbül
AuthorMarijana Hadzima-Nyarko
AuthorErcan Işık
AuthorRabia İzol
AuthorDorin Radu
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSustainability
Numberingvol. 15, no. 2, 9715
e-ISSN2071-1050
DOIhttps://doi.org/10.3390/su15129715
URN:NBNurn:nbn:hr:133:272681
Publication2023
Document URLhttps://www.mdpi.com/2071-1050/15/12/9715
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-17 13:21:45