Seismic Damage Prediction for Non-Structural Building Systems: a Framework Based on Building Information Modeling and Machine Learning, M.Sc. Thesis Sharif University of Technology ; Alvanchi, Amin (Supervisor)
Abstract
Despite the vulnerability of non-structural systems in buildings to disasters, their resilient design has received minimal attention from the practitioners of the construction industry. However, interruption in the performance of these systems jeopardizes the functionality of the buildings and threatens the resilience of the whole community. To address this issue, the present study proposes a novel framework for predicting possible damage states of non-structural building systems under disasters. The proposed framework benefits from an automated combination of Building Information Modeling (BIM) as a visualized 3D database of the building's components and the Machine Learning (ML)...
Cataloging briefSeismic Damage Prediction for Non-Structural Building Systems: a Framework Based on Building Information Modeling and Machine Learning, M.Sc. Thesis Sharif University of Technology ; Alvanchi, Amin (Supervisor)
Abstract
Despite the vulnerability of non-structural systems in buildings to disasters, their resilient design has received minimal attention from the practitioners of the construction industry. However, interruption in the performance of these systems jeopardizes the functionality of the buildings and threatens the resilience of the whole community. To address this issue, the present study proposes a novel framework for predicting possible damage states of non-structural building systems under disasters. The proposed framework benefits from an automated combination of Building Information Modeling (BIM) as a visualized 3D database of the building's components and the Machine Learning (ML)...
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