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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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, br. 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 i 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, br. 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, i 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. [pristupljeno 14.01.2025.];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, br. 2, 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:272681. [Citirano: 14.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:272681
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Naslov (engleski)A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
AutorMehmet Fatih Işık
AutorFatih Avcil
AutorEhsan Harirchian
AutorMehmet Akif Bülbül
AutorMarijana Hadzima-Nyarko
AutorErcan Işık
AutorRabia İzol
AutorDorin Radu
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaSustainability
Brojčani podacivol. 15, br. 2, 9715
e-ISSN2071-1050
DOIhttps://doi.org/10.3390/su15129715
URN:NBNurn:nbn:hr:133:272681
Datum objave publikacije2023
URL dokumentahttps://www.mdpi.com/2071-1050/15/12/9715
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-17 13:21:45