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Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

Author

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  • Ehsan Harirchian

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany)

  • Tom Lahmer

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany)

  • Shahla Rasulzade

    (Research Group Theoretical Computer Science/Formal methods, School of Electrical Engineering and Computer Science, Universität Kassel, Wilhelmshöher Allee 73, D-34131 Kassel, Germany)

Abstract

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.

Suggested Citation

  • Ehsan Harirchian & Tom Lahmer & Shahla Rasulzade, 2020. "Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network," Energies, MDPI, vol. 13(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2060-:d:348085
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    References listed on IDEAS

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    1. Jiangang Hao & Tin Kam Ho, 2019. "Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 348-361, June.
    2. Mehmet Inel & Sevket Senel & Selcuk Toprak & Yasemin Manav, 2008. "Seismic risk assessment of buildings in urban areas: a case study for Denizli, Turkey," 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. 46(3), pages 265-285, September.
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    Cited by:

    1. Ehsan Harirchian & Tom Lahmer & Vandana Kumari & Kirti Jadhav, 2020. "Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings," Energies, MDPI, vol. 13(13), pages 1-15, June.
    2. Angelo Cardellicchio & Sergio Ruggieri & Valeria Leggieri & Giuseppina Uva, 2021. "View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings," Data, MDPI, vol. 7(1), pages 1-14, December.

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