TY  - JOUR
A1  - Harirchian, Ehsan
A1  - Kumari, Vandana
A1  - Jadhav, Kirti
A1  - Raj Das, Rohan
A1  - Rasulzade, Shahla
A1  - Lahmer, Tom
T1  - A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
T2  - Applied Sciences
N2  - Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.
KW  - Erdbeben
KW  - Vulnerability
KW  - Earthquake
KW  - damaged buildings
KW  - earthquake safety assessment
KW  - soft computing techniques
KW  - rapid visual screening
KW  - Machine Learning
KW  - vulnerability assessment
KW  - OA-Publikationsfonds2020
Y1  - 2020
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4274
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20201022-42744
UR  - https://www.mdpi.com/2076-3417/10/20/7153
VL  - 2020
IS  - Volume 10, issue 20, article 7153
PB  - MDPI
CY  - Basel
ER  -