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

  • 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. TheThe 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.show moreshow less

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    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Ehsan HarirchianORCiD, Tom LahmerORCiDGND, Vandana KumariORCiD, Kirti JadhavORCiD
DOI (Cite-Link):https://doi.org/10.3390/en13133340Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200707-41915Cite-Link
URL:https://www.mdpi.com/1996-1073/13/13/3340
Parent Title (English):Energies
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/06/30
Date of first Publication:2020/06/30
Release Date:2020/07/07
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 13, issue 13, 3340
Pagenumber:15
Tag:buildings; earthquake vulnerability assessment; machine learning; rapid visual screening; support vector machine
GND Keyword:Erdbeben; Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
500 Naturwissenschaften und Mathematik / 550 Geowissenschaften, Geologie / 551 Geologie, Hydrologie, Meteorologie
BKL-Classification:38 Geowissenschaften / 38.38 Seismologie
54 Informatik / 54.72 Künstliche Intelligenz
56 Bauwesen / 56.12 Betonbau
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)