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BIM and machine learning in seismic damage prediction for non-structural exterior infill walls
, Article Automation in Construction ; Volume 139 , 2022 ; 09265805 (ISSN) ; TohidiFar, A ; Alvanchi, A ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Despite the seismic vulnerability of non-structural Exterior Infill Walls (EIWs), their resilient design has received minimal attention. This study addresses the issue by proposing a novel framework for predicting possible damage states of EIWs. The framework benefits from an automated combination of Building Information Modeling as a visualized 3D database of the building's components and the Machine Learning classification as the prediction engine. The framework's applicability is studied in a Proof of Concept example of the exterior walls of the buildings damaged in the 2017 earthquake in Kermanshah, Iran. The Extremely Randomized Trees classifier produced the best results for predicting...