Machine Learning for Predicting Prehurricane Structural Damage

This study proposes a novel framework for the prediction of structural damages caused by extreme weather and climate events. In current practice, following a weather event, inspectors manually evaluate damaged structures and assign a damage state classification according to FEMA guidelines. The application of machine learning methods to postevent damage classification has received significant attention in the past decade. Current state-of-the-art applications in automating the assigning of damage states have focused on postevent unmanned aerial system (UAS)-driven image classification. These works have achieved moderate success using damage classes with varying similarities to established FEMA guidelines. This work proposes a framework for predicting FEMA damage states at a single structure level prior to an event. Using a precurated data set of structural characteristics and predicted best track storm data, a novel approach can be used to optimize postevent response efforts. The methodology was validated using a data set of structural features and best track storm data gathered following Hurricanes Harvey, Michael, Irma, and Dorian. The trained model achieved a 48.08% single damage state classification accuracy and an 84.24%±1 class damage state classification accuracy. These results show that the proposed framework can perform pre-event damage prediction with performance on par with the current postevent damage classification methods.