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    Multihazard hurricane fragility model for wood structure homes considering hazard parameters and building attributes interaction.

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    Author
    Massarra, C. C.; Friedland, C. J.; Marx, B. D.; Dietrich, J. C.
    Abstract
    Predicting building damage as a function of hurricane hazards, building attributes, and the interaction between hazard and building attributes is a key to understanding how significant interaction reflects variation hazard intensity effect on damage based on building attribute levels. This paper develops multihazard hurricane fragility models for wood structure homes considering interaction between hazard and building attributes. Fragility models are developed for ordered categorical damage states (DS) and binary collapse/no collapse. Exterior physical damage and building attributes from rapid assessment in coastal Mississippi following Hurricane Katrina (2005), high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN+ADCIRC) models, and base flood elevation values are used as model input. Leave-one-out cross-validation (LOOCV) is used to evaluate model prediction accuracy. Eleven and forty-nine combinations of global damage response variables and main explanatory variables, respectively, were investigated and evaluated. Of these models, one DS and one collapse model met the rejection criteria. These models were refitted considering interaction terms. Maximum 3-s gust wind speed and maximum significant wave height were found to be factors that significantly affect damage. The interaction between maximum significant wave height and number of stories was the significant interaction term for the DS and collapse models. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of being in a higher rather than in a lower damage state for DS model were found to be 1.95 times greater for one- rather than for two-story buildings. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of collapse were found to be 2.23 times greater for one- rather than for two-story buildings. Model prediction accuracy was 84% and 91% for DS and collapse models, respectively. This paper does not consider the full hazard intensity experienced in Hurricane Katrina; rather, it focuses on single-family homes in a defined study area subjected to wind, wave, and storm surge hazards. Thus, the findings of this paper are not applicable for events with hazards that exceed those experienced in the study area, from which the models were derived.
    Description
    This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
    URI
    http://hdl.handle.net/10342/9437
    Subject
     hurricane; predictive fragility models; interaction; multi-hazard; wood framed structure 
    Date
    2020-09-22
    Citation:
    APA:
    Massarra, C. C., & Friedland, C. J., & Marx, B. D., & Dietrich, J. C.. (September 2020). Multihazard hurricane fragility model for wood structure homes considering hazard parameters and building attributes interaction.. , (), - . Retrieved from http://hdl.handle.net/10342/9437

    Display/Hide MLA, Chicago and APA citation formats.

    MLA:
    Massarra, C. C., and Friedland, C. J., and Marx, B. D., and Dietrich, J. C.. "Multihazard hurricane fragility model for wood structure homes considering hazard parameters and building attributes interaction.". . . (), September 2020. December 11, 2023. http://hdl.handle.net/10342/9437.
    Chicago:
    Massarra, C. C. and Friedland, C. J. and Marx, B. D. and Dietrich, J. C., "Multihazard hurricane fragility model for wood structure homes considering hazard parameters and building attributes interaction.," , no. (September 2020), http://hdl.handle.net/10342/9437 (accessed December 11, 2023).
    AMA:
    Massarra, C. C., Friedland, C. J., Marx, B. D., Dietrich, J. C.. Multihazard hurricane fragility model for wood structure homes considering hazard parameters and building attributes interaction.. . September 2020; (): . http://hdl.handle.net/10342/9437. Accessed December 11, 2023.
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