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A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

  • Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service.Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.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 HarirchianGND, Vandana KumariORCiDGND, Kirti JadhavORCiD, Rohan Raj DasORCiD, Shahla RasulzadeORCiD, Tom LahmerORCiDGND
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20201022-42744Cite-Link
URL:https://www.mdpi.com/2076-3417/10/20/7153
DOI (Cite-Link):https://doi.org/10.3390/app10207153Cite-Link
Parent Title (English):Applied Sciences
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/10/14
Date of first Publication:2020/10/14
Release Date:2020/10/22
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 10, issue 20, article 7153
Page Number:18
Tag:Earthquake; Machine Learning; Vulnerability; damaged buildings; earthquake safety assessment; rapid visual screening; soft computing techniques; vulnerability assessment
GND Keyword:Erdbeben
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
BKL-Classification:56 Bauwesen
:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)