Predicting Structural Response of Steel Building under Ground Motion Excitation using Deep Learning Networks, M.Sc. Thesis Sharif University of Technology ; Mohtasham Dolatshahi, Kiarash (Supervisor) ; Yazdanpanah, Omid (Supervisor)
Abstract
This paper aims at producing surrogate models which can predict building structural response under ground motion loads. Rapid response prediction has a great influence on post-event decision-making. The current study follows mentioned purpose in two main sections. The first section proposed deep models, able at estimating displacement time-series response by using only ground motion and roof acceleration. By this point, different preprocessing methods and their effects are studied. Also, a novel loss function is introduced and a hybrid model consists of different deep layers utilized to gain accurate models. These models train and evaluate on two case-study buildings; a special moment frame...
Cataloging briefPredicting Structural Response of Steel Building under Ground Motion Excitation using Deep Learning Networks, M.Sc. Thesis Sharif University of Technology ; Mohtasham Dolatshahi, Kiarash (Supervisor) ; Yazdanpanah, Omid (Supervisor)
Abstract
This paper aims at producing surrogate models which can predict building structural response under ground motion loads. Rapid response prediction has a great influence on post-event decision-making. The current study follows mentioned purpose in two main sections. The first section proposed deep models, able at estimating displacement time-series response by using only ground motion and roof acceleration. By this point, different preprocessing methods and their effects are studied. Also, a novel loss function is introduced and a hybrid model consists of different deep layers utilized to gain accurate models. These models train and evaluate on two case-study buildings; a special moment frame...
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