Wind-induced loss modeling plays a key role in insurance risk management. Hence, a flexible vulnerability framework is to be developed for residential and commercial buildings. This model predicts the losses induced by hurricane wind pressure, wind-borne debris and wind-driven rain. Twenty-five different key variables of the buildings and environment are used as attributes for the simulations. Model results are validated using the Florida Public Hurricane Loss Models (FPHLM) and HAZUS wind vulnerability functions. New contributions include (1) a Markovian roof-aging model to address decreases in roof performance due to aging, and (2) occupancy-specific interior value models based on FEMA Normative quantities for the systematic evaluation of interior value applicable to archetype buildings. A simple wind debris impact model and wind-driven rain intrusion model is also introduced. The influence of the number of stories, roof aging, and window vulnerability resulting in damage are investigated in this article to ensure consistency of the results. The proposed framework enables insurance loss modelers to make judicious choices of input variables based on partial or detailed knowledge about the building to model losses. Future research should focus on validation and calibration using good-quality insurance claims data.
SUMMARYWhen the individual PDFs of closely-spaced random variables such as natural frequencies of a structure overlap, generation of sample sets by assuming the frequencies to be independent random variables can lead to incorrect sets of frequencies in the sense that the frequencies do not remain as ordered sets. Rejection of such disordered sample sets results in individual density functions that are significantly different from the distributions initially assumed for sampling each random variable. One way to overcome this constraint in the simulation of an ordered set of random variables is to consider them in an implicit manner using a joint PDF. In this paper, we present a formulation for a joint density function that is developed using fundamental probability approaches. The formulation ensures that the sampled random variables always remain as ordered sets and maintain the individual density functions for each variable. Application of the proposed formulation is illustrated for cases with not just two closely-spaced variables but also for a case with multiple closely-spaced variables such that the PDFs of more than two random variables overlap with each other. An expression is presented to determine the exact number of terms needed in the formulation. However, it is also illustrated that only two terms are sufficient in most applications even when the exact number of terms needed is very high.
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