TY  - JOUR
A1  - Harirchian, Ehsan
A1  - Kumari, Vandana
A1  - Jadhav, Kirti
A1  - Rasulzade, Shahla
A1  - Lahmer, Tom
A1  - Raj Das, Rohan
T1  - A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
T2  - Applied Sciences
N2  - 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’ 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.
KW  - Maschinelles Lernen
KW  - Neuronales Netz
KW  - Machine learning
KW  - Building safety assessment
KW  - artificial neural networks
KW  - supervised learning
KW  - damaged buildings
KW  - rapid classification
KW  - OA-Publikationsfonds2021
Y1  - 2021
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4485
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210818-44853
UR  - https://www.mdpi.com/2076-3417/11/16/7540
VL  - 2021
IS  - Volume 11, issue 16, article 7540
SP  - 1
EP  - 33
PB  - MDPI
CY  - Basel
ER  -