A Machine Learning Model for Simplified Damage and Loss Assessment of the Buildings After an Earthquake, M.Sc. Thesis Sharif University of Technology ; Mohtasham Dolatshahi, Kiarash (Supervisor)
Abstract
This study presents a Machine Learning framework for post-earthquake structural damage state and economic loss estimation. The framework is proposed to rapidly determine whether a structure is safe or unsafe to be occupied following a seismic event, to predict the detailed structural response, and finally to evaluate the economic loss of the buildings. An extensive database is developed by analyzing twelve low-rise, mid-rise, and high-rise eccentrically braced frames (EBFs) under 44 scaled ground motion records through incremental dynamic analysis (IDA) to train the Machine Learning algorithms. Machine Learning algorithms are employed to classify the structural damage state of the buildings...
Cataloging briefA Machine Learning Model for Simplified Damage and Loss Assessment of the Buildings After an Earthquake, M.Sc. Thesis Sharif University of Technology ; Mohtasham Dolatshahi, Kiarash (Supervisor)
Abstract
This study presents a Machine Learning framework for post-earthquake structural damage state and economic loss estimation. The framework is proposed to rapidly determine whether a structure is safe or unsafe to be occupied following a seismic event, to predict the detailed structural response, and finally to evaluate the economic loss of the buildings. An extensive database is developed by analyzing twelve low-rise, mid-rise, and high-rise eccentrically braced frames (EBFs) under 44 scaled ground motion records through incremental dynamic analysis (IDA) to train the Machine Learning algorithms. Machine Learning algorithms are employed to classify the structural damage state of the buildings...
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