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Recent advances in post-windstorm reconnaissance have accelerated the amounts of perishable building performance data being collected after extreme windstorms, necessitating better frameworks for knowledge discovery from the data. One particularly promising approach to this need is Bayesian Networks (BN), which have grown in their application in natural hazards research due to their ability to explicitly model causal factors. In this study, a Naïve Bayes Network (NBN) was first developed to observe the influence of wind speed ratio, roof shape, number of stories, roof cover, and pre/post-IBC (2002) on the damage class of a structure and predict the probability of each damage class given a specified scenario. This initial model was derived solely from empirical data and the parameters of influence are modelled with conditional independence, and limiting the model’s use. An illustrative hybrid Bayesian Network is also proposed which combines empirical data, known wind engineering theory, and expert opinion to formulate a more holistic model of structural performance in windstorms better suited for parameter inference and building performance predictions. 

This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning.