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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

Author

Listed:
  • 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
    Faculty of Civil Engineering, Transilvania University of Brasov, Turnului Street, 500152 Brasov, Romania)

  • 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)

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.

Suggested Citation

  • Mehmet Fatih Işık & Fatih Avcil & Ehsan Harirchian & Mehmet Akif Bülbül & Marijana Hadzima-Nyarko & Ercan Işık & Rabia İzol & Dorin Radu, 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, MDPI, vol. 15(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9715-:d:1173575
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    References listed on IDEAS

    as
    1. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić, 2020. "A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    2. Mehmet Alpyürür & Musaffa Ayşen Lav, 2022. "An assessment of probabilistic seismic hazard for the cities in Southwest Turkey using historical and instrumental earthquake catalogs," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 335-365, October.
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    Cited by:

    1. Ming-Tsung Hung & Huai-Chun Lo, 2024. "Risk Analysis of Mortgage Loan Default for Bank Customers and AI Machine Learning," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(6), pages 1-3.
    2. Qi Xiang & Zhaoming Yang & Yuxuan He & Lin Fan & Huai Su & Jinjun Zhang, 2023. "Enhanced Method for Emergency Scheduling of Natural Gas Pipeline Networks Based on Heuristic Optimization," Sustainability, MDPI, vol. 15(19), pages 1-18, September.

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