Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings

  • The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. ArtificialThe seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiencyshow 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: Vandana KumariORCiD, Dr.-Ing. Ehsan HarirchianORCiDGND, Prof. Dr. Tom LahmerORCiDGND, Shahla RasulzadeORCiD
DOI (Cite-Link):https://doi.org/10.3390/buildings12050578Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220509-46387Cite-Link
URL:https://www.mdpi.com/2075-5309/12/5/578
Parent Title (English):Buildings
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2022/04/29
Date of first Publication:2022/04/29
Release Date:2022/05/09
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2022
Issue:Volume 12, issue 5, article 578
Pagenumber:23
First Page:1
Last Page:23
Tag:Machine learning; Vulnerability assessment; rapid assessment
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:56 Bauwesen
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2022
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)