Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
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Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany
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Research Group Theoretical Computer Science/Formal Methods, School of Electrical Engineering and Computer Science, Universität Kassel, Wilhelmshöher Allee 73, 34131 Kassel, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Fulvio Parisi
Buildings 2022, 12(5), 578; https://doi.org/10.3390/buildings12050578
Received: 4 April 2022
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Revised: 24 April 2022
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Accepted: 26 April 2022
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Published: 29 April 2022
(This article belongs to the Special Issue Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques)
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. 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 efficiency.
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MDPI and ACS Style
Kumari, V.; Harirchian, E.; Lahmer, T.; Rasulzade, S. Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings. Buildings 2022, 12, 578. https://doi.org/10.3390/buildings12050578
AMA Style
Kumari V, Harirchian E, Lahmer T, Rasulzade S. Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings. Buildings. 2022; 12(5):578. https://doi.org/10.3390/buildings12050578
Chicago/Turabian StyleKumari, Vandana, Ehsan Harirchian, Tom Lahmer, and Shahla Rasulzade. 2022. "Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings" Buildings 12, no. 5: 578. https://doi.org/10.3390/buildings12050578
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