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

Autori
Işık, Mehmet Fatih ; Avcil, Fatih ; Harirchian, Ehsan ; Bülbül, Mehmet Akif ; Hadzima-Nyarko, Marijana ; Işık, Ercan ; İzol, Rabia ; Radu, Dorin

Kolaboracija
Department of Electrical-Electronics Engineering, Hitit University, Çorum, Türkiye ; Department of Civil Engineering, Bitlis Eren University, Bitlis 13100, Türkiye ; Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, Weimar, Germany ; Department of Computer Engineering, Nevşehir Hacı Bektaş Veli University, Nevşehir, Türkiye ; Faculty of Civil Engineering, Transilvania University of Brasov, Turnului Street, Brasov, Romania ; Department of Civil Engineering, Middle East Technical University, Ankara, Türkiye

Izvornik
Sustainability (2071-1050) 15 (2023), 12; 9715, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
mid-rise ; regular RC building ; target displacement ; ANN ; optimization algorithm
(C building ; target displacement ; ANN ; optimization algorithm)

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

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