Rapid Seismic Damage Estimation using Bayesian System Identification, Ph.D. Dissertation Sharif University of Technology ; Mahsuli, Mojtaba (Supervisor) ; Ghahari, Farid (Co-Supervisor)
Abstract
This dissertation presents a comprehensive probabilistic framework based on system identification for rapid seismic damage assessment of buildings at regional scale. Given the large number of buildings within a region and the need for rapid damage detection, this research uses simplified models with low computational cost to model each building. For this purpose, stochastic filters are employed as system identification tools. In the first step, a continuous linear model consisting of a Timoshenko beam in combination with the extended Kalman filter is utilized. This model is subjected to joint state-parameter identification under both fixed- and flexible-base conditions. Additionally, the...
Cataloging briefRapid Seismic Damage Estimation using Bayesian System Identification, Ph.D. Dissertation Sharif University of Technology ; Mahsuli, Mojtaba (Supervisor) ; Ghahari, Farid (Co-Supervisor)
Abstract
This dissertation presents a comprehensive probabilistic framework based on system identification for rapid seismic damage assessment of buildings at regional scale. Given the large number of buildings within a region and the need for rapid damage detection, this research uses simplified models with low computational cost to model each building. For this purpose, stochastic filters are employed as system identification tools. In the first step, a continuous linear model consisting of a Timoshenko beam in combination with the extended Kalman filter is utilized. This model is subjected to joint state-parameter identification under both fixed- and flexible-base conditions. Additionally, the...
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