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Wind fragility curves for roof sheathing were developed for single-family building models to investigate the effects of roof shape and roof pitch on the wind performance of roof sheathing. For gable roofs, it was found that more complex roof shapes are more likely to suffer roof sheathing damage when subjected to high winds. The probability of no roof sheathing failure can be up to 36% higher for a simple gable roof than for a complex gable roof. For hip roofs with different configurations, variation in roof shape has minimal effect on roof sheathing fragility. Roof pitch effects were also evaluated for 10 pitch angles, ranging from 14° to 45°. Results suggest that for roof pitches smaller than 27°, the effects of this angle are more substantial on the performance of gable roofs than on hip roofs. For gable roofs, the probability of no roof sheathing failure can be up to 23% higher for a 23° roof pitch than that for an 18° roof pitch. Furthermore, the inclusion of complex roof shapes in a regional hurricane loss model for New Hanover County, North Carolina, accounted for a 44% increase in estimated annual expected losses from roof sheathing damages compared to a scenario in which all roofs are assumed to have rectangular roof shapes. Therefore, to avoid an underestimation of roof damages due to high-wind impact, the inclusion of complex roof geometries in hurricane loss modeling is strongly recommended.
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Using Bayesian Networks for Structured Learning from Post-Windstorm Building Performance
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.
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- Award ID(s):
- 1944149
- PAR ID:
- 10450666
- Date Published:
- Journal Name:
- 16th International Conference on Wind Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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