Journal
BUILDINGS
Volume 12, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/buildings12050578
Keywords
rapid assessment; machine learning; seismic vulnerability; Django; damage classification
Funding
- German Research Foundation (DFG)
- Bauhaus-Universitat Weimar within the Open-Access Publishing Programme
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This study investigates the use of various non-parametric algorithms for Rapid Visual Screening (RVS) by applying different earthquake datasets. It also encourages vulnerability research based on factors related to building importance and exposure. Additionally, a web-based application built on Django is introduced to facilitate real-time seismic vulnerability investigation.
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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