BIM and machine learning in seismic damage prediction for non-structural exterior infill walls

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 new cases with an overall accuracy of %86. The trained model is used to develop a system for predicting the damage states of EIWs of a new building. The proposed framework works as a complementary tool in buildings' design and operation phases to enhance EIWs' seismic resilience.