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

Mehmet Fatih Işık (), Fatih Avcil, Ehsan Harirchian, Mehmet Akif Bülbül, Marijana Hadzima-Nyarko (), Ercan Işık, Rabia İzol and Dorin Radu
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Mehmet Fatih Işık: Department of Electrical-Electronics Engineering, Hitit University, Çorum 19030, Türkiye
Fatih Avcil: Department of Civil Engineering, Bitlis Eren University, Bitlis 13100, Türkiye
Ehsan Harirchian: Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany
Mehmet Akif Bülbül: Department of Computer Engineering, Nevşehir Hacı Bektaş Veli University, Nevşehir 50300, Türkiye
Marijana Hadzima-Nyarko: Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia
Ercan Işık: Department of Civil Engineering, Bitlis Eren University, Bitlis 13100, Türkiye
Rabia İzol: Department of Civil Engineering, Middle East Technical University, Ankara 06100, Türkiye
Dorin Radu: Faculty of Civil Engineering, Transilvania University of Brasov, Turnului Street, 500152 Brasov, Romania

Sustainability, 2023, vol. 15, issue 12, 1-18

Abstract: 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: mid-rise; regular RC building; target displacement; ANN; optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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