Predictive multi-hazard hurricane data-based fragility model for residential homes

Massarra, Carol; Friedland, Carol J.; Marx, Brian D.; Dietrich, J. C.

doi:10.1016/j.coastaleng.2019.04.008

Cited by 15 publications

(7 citation statements)

References 59 publications

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“…Building footprint polygons are from the City of Biloxi, City of Gulfport, The WF Damage Scale categorizes global combined wind and flood residential building damage into seven damage classes ranked WF0 through WF6. The WF Damage Scale has been previously adopted by Massarra et al (2019) and modified and applied by Hatzikyriakou (2017), Tomiczek et al (2017), and Zhang et al (2017) to classify building damage data obtained from field reconnaissance. A database that includes land parcel data and building footprint polygons was developed using a geographic information systems (ArcGIS).…”

Section: Global Building Damage State and Building Attributes Variablesmentioning

confidence: 99%

“…For the uncertainties associated with damage assessment and to ensure that damage levels recorded by one assessor match those that would be recorded by another assessor, buildings in the study area were assessed by two assessors. A confusion matrix showing assessment results was then developed (Massarra et al, 2019), where a cross-classification rate (CCR) of 99% was found between the two assessors.…”

Section: Global Building Damage State and Building Attributes Variablesmentioning

confidence: 99%

“…In this study, a special case of k-fold cross-validation, known as leave-one-out cross-validation (LOOCV), where k is equal to the sample size (N) is used. Recently, this method has been used to evaluate performance of fragility models for buildings subjected to tsunami (Macabuag et al, 2016), earthquake (Mai et al, 2017), and hurricane hazards (Massarra et al, 2019) and for line towers subjected to wind (Cai et al, 2019). Models satisfied the rejection (criteria 1 and 2) and assessment (criteria a and b) criteria and are fitted N times, leaving one observation out at each fit.…”

Section: Model Validationmentioning

confidence: 99%

“…Current models are more comprehensive than other models, with only hazard parameters including previous models developed by the authors (Massarra et al, 2019). The models predict the probability of being in or exceeding a certain damage state and probability of collapse as a function of hazard parameters, building attributes, and their interactions and explain the effect of these variables and their interactions on damage that cannot be explained by fragility models based on just hazard parameters.…”

Section: Interpretation Of Interaction Terms Of Ds and Collapse Fragimentioning

confidence: 99%

“…Tomiczek et al (2014a) developed a multivariate regression fragility model for pileelevated wood structures to estimate the probability of collapse as a function of H (i.e., maximum current velocity, breaking wave height, maximum significant wave height) and A (i.e., freeboard height, building age). Massarra et al (2019) developed a predictive data-based fragility models to predict the probabilities of a home being in or exceeding a certain damage state and complete failure as a function of H (i.e., maximum significant wave height, maximum 3-s gust wind speed, and maximum water speed). Specific to coastal building subjected to storm surge, Hatzikyriakou et al (2015) developed a logistic regression fragility model for single-family home component to predict the probability of collapse as a function of E (i.e., distance from the coast, ground elevation,) and A (i.e., elevation of the lowest horizontal member, structure height above lowest horizontal member, house age, building perimeter), and Hatzikyriakou and Lin (2018) developed a cumulative logit fragility model to predict the probability of a home being in or exceeding a certain damage state as a function of H (i.e., flood inundation, wave height, dune erosion) and E (i.e., base flood elevation) with the exclusion of building attributes (A).…”

Section: Introductionmentioning

confidence: 99%

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Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attributes Interaction

Massarra

Friedland

Marx

et al. 2020

Front. Built Environ.

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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.

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Section: Global Building Damage State and Building Attributes Variablesmentioning

confidence: 99%

Section: Global Building Damage State and Building Attributes Variablesmentioning

confidence: 99%