Predicting the vulnerability of typical residential buildings to hurricane damage

Hurricanes have caused billions of dollars in losses in the United States and could devastate up to {dollar}1.5 trillion worth of existing structures in Florida alone. The population density on Florida's 1200-mile coastline continues to grow, and potential losses will continue to mount. The insurance industry and the Florida insurance regulatory agency both require a means of estimating these expected losses. Only a handful of studies exist in the public domain to predict aggregate hurricane damage. Most published studies use regression techniques with post-disaster investigations or claims data to develop vulnerability curves. This approach is highly dependent on the type of construction common to the areas represented in the data, thus limiting the predictive capabilities to regions of similar construction. A promising approach used by one commercial model estimates vulnerability by explicitly accounting for the resistance capacity of building components and load produced by wind. This so-called component approach applies claims data from previous storms as a validation (rather than calibration) tool, and can be readily adapted to different regions with varying predominant construction.; The Florida Department of Insurance (FDOI) sponsored the development of a public hurricane risk model. The goal of this ongoing project is to predict hurricane wind-induced insurance losses by zip code for the State of Florida, on an annualized basis and for predefined scenarios. The engineering team is responsible for relating specific wind speeds to predicted losses for typical residential buildings in the state of Florida. Our study developed a probabilistic model predicting structural damage from hurricane winds in Florida. The core of this model is a Monte Carlo Simulation engine that generates damage information for typical Florida homes, using a component approach. The simulation compares deterministic wind loads, and the probabilistic capacity of vulnerable building components to resist these loads, to determine the probability of damage. In this manner, probabilistic structural damage is identified over a range of assigned wind speeds. Monetary loss associated with structural damage and the likelihood of occurrence for discrete wind speeds will be determined by models under development by other groups in the project.