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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info:eu-repo/semantics/openAccessTarih
2023Yazar
Işık, Mehmet FatihAvcil, Fatih
Harirchian,Ehsan
Akif Bülbül, Mehmet
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(12), 9715.Özet
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.
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