Machine Learning for Predicting Prehurricane Structural Damage

Abstract

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. This study provides a framework for the prediction of hurricane-induced structural damage states prior to an event. Based on a lightweight artificial intelligence model, the framework is designed to be accessible to homeowners, municipalities, and relief organizations that do not have access to sophisticated hardware. The framework utilizes structural characteristics and storm information to generate structure-by-structure damage predictions based on FEMA hurricane damage states. A data set consisting of structures impacted by Hurricanes Harvey, Michael, Irma, and Dorian was used to validate the framework and provide an estimate of its capabilities. The trained model achieved an overall single state classification accuracy of 48.08% and a ±1 class accuracy of 84.24%. These results show that the proposed framework can provide homeowners with prehurricane predictions of the damage state their unique home is likely to suffer with the same level of performance as current state-of-the-art image-based postevent damage classification artificial intelligence models.