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Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Author

Listed:
  • Ehsan Harirchian

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany)

  • Tom Lahmer

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany)

  • Vandana Kumari

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany)

  • Kirti Jadhav

    (Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany)

Abstract

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3340-:d:378642
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    References listed on IDEAS

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    1. 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.
    2. Ningthoujam Monika Chanu & Radhikesh Prasad Nanda, 2018. "A Proposed Rapid Visual Screening Procedure for Developing Countries," International Journal of Geotechnical Earthquake Engineering (IJGEE), IGI Global, vol. 9(2), pages 38-45, July.
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    Cited by:

    1. Onur Coskun & Alper Aldemir, 2023. "Machine learning network suitable for accurate rapid seismic risk estimation of masonry building stocks," 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. 115(1), pages 261-287, January.

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