TY  - JOUR
A1  - Kumari, Vandana
A1  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Rasulzade, Shahla
T1  - Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
T2  - Buildings
N2  - 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
KW  - Maschinelles Lernen
KW  - rapid assessment
KW  - Machine learning
KW  - Vulnerability assessment
KW  - OA-Publikationsfonds2022
Y1  - 2022
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4638
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220509-46387
UR  - https://www.mdpi.com/2075-5309/12/5/578
VL  - 2022
IS  - Volume 12, issue 5, article 578
SP  - 1
EP  - 23
PB  - MDPI
CY  - Basel
ER  -