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Hurricane Damage Prediction Model for Residential Structures



Emil Simiu, J P. Pinelli, C Subramanian, L. Zhang, A Cope, K Gurley, James J. Filliben, S Hamid


This paper reports progress in the development of a practical probabilistic model for the estimation of expected annual damage induced by hurricane winds in residential structures. The estimation of the damage is accomplished in several steps. First, basic damage modes for components of specific building types are defined, and their probabilities of occurrence as functions of wind speeds are estimated. Second, the damage modes are combined in possible damage states, whose probabilities of occurrence are calculated from the probabilities of the basic damage modes and estimated conditional probabilities as measures of the mutual dependence between those modes. The paper describes the conceptual framework for the proposed model, and illustrates its application for a specific building type with hypothetical probabilistic input. Actual probabilistic input must be based on laboratory studies, post-damage surveys, insurance claims data, engineering analyses and judgment, and Monte Carlo simulation methods. The proposed component-based model is flexible and transparent. it is therefore capable of being readily scrutinized. The model can be used in conjunction with historical loss data, to which it can readily be calibrated.
Proceedings Title
International Conference on Wind Engineering | 11th | | Wind Science and Engineering Research Center
No. 11
Conference Dates
June 2-5, 2003
Conference Title
Journal of Structural Engineering


Building Technology, directionality (wind), extreme values, meteorology, structural engineering, wind


Simiu, E. , Pinelli, J. , Subramanian, C. , Zhang, L. , Cope, A. , Gurley, K. , Filliben, J. and Hamid, S. (2004), Hurricane Damage Prediction Model for Residential Structures, International Conference on Wind Engineering | 11th | | Wind Science and Engineering Research Center, [online], (Accessed May 22, 2024)


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Created November 1, 2004, Updated February 19, 2017