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Use of machine learning algorithms for damage estimation of reinforced concrete buildings
Identifying the vulnerabilities in a building is a crucial step towards earthquake risk mitigation. Rapid visual screening is a quick and popular method for seismic vulnerability assessment. It helps identify buildings that require detailed investigation, which is done by modelling using seismic analysis software. This is a time-consuming and resource-intensive task. This article proposes the use of machine learning to bypass the seismic analysis of buildings. A case study using 1296 building models and maximum inter-storey drift ratio as the measure of damage has been presented. Random forest gives the best prediction accuracy in the study.
Keywords
Damage estimation, earthquakes, machine learning, rapid visaul screening, reinforced concrete building
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