Risk-based wind loss and mitigation for residential wood framed construction

As a result of increasing windstorm losses in the United States over the past 50 years, a variety of residential wind hazard mitigation methods have been suggested. Mitigation undoubtedly reduces windstorm losses; however, the expected economic risk reduction of mitigation practices over the life of the building depends on the building characteristics (i.e., capacity) and the intensity and occurrence of wind speeds (i.e., demand). Effective decision making requires estimation of potential future losses based on many variables. Many models, primarily mechanics-based simulation models, have been developed to predict building damage from wind events; however; fewer models of economic loss have been developed, although economic losses are more easily quantified over a spatial domain and have the potential for more effective widespread use. Additionally, many existing models consider damage and loss as a function of basic wind speed in open terrain and few address the variation in loss due to changes in surface roughness, although surface roughness is a critical component in surface wind speed. In spite of advancements in damage and loss modeling, the limitations of existing publications (e.g., geographically limited in scope, limited to specific building types, limited to specific events, limited to open terrain) prevent generalization and application of the results on a nationwide basis to support development of a mitigation decision-making framework.To address these limitations, this research presents a methodology to calculate tabular expected annual loss (EAL) results for 160 variations of one-story, single-family homes at each ASCE 7-10 wind contour through Monte Carlo simulation of local annual maximum wind speeds convolved with Hazus-MH economic loss functions for open terrain and non-open terrain. The results are integrated into a decision-making framework designed to provide customized consumer-level guidance to assist the mitigation decision-making process based on location, terrain, years of interest, and building configuration. The results provide practical results in an easy-to-use format to facilitate consumer-level mitigation decision making.