A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

  • A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ presentA vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.show moreshow less

Download full text files

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

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Document Type:Article
Author:Dr.-Ing. Ehsan HarirchianORCiDGND, Vandana KumariORCiD, Kirti JadhavORCiD, Shahla RasulzadeORCiD, Prof. Dr. rer. nat. Tom LahmerORCiDGND, Rohan Raj DasORCiD
DOI (Cite-Link):https://doi.org/10.3390/app11167540Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210818-44853Cite-Link
URL:https://www.mdpi.com/2076-3417/11/16/7540
Parent Title (German):Applied Sciences
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2021/08/17
Date of first Publication:2021/08/17
Release Date:2021/08/18
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2021
Issue:Volume 11, issue 16, article 7540
Pagenumber:33
First Page:1
Last Page:33
Tag:Building safety assessment; Machine learning; artificial neural networks; damaged buildings; rapid classification; supervised learning
GND Keyword:Maschinelles Lernen; Neuronales Netz
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
BKL-Classification:56 Bauwesen
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2021
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)