Hurricane Damage Prediction Model for Residential Structures

Pinelli, Jean‐Paul; Simiu, Emil; Gurley, Kurtis R.; Subramanian, Chelakara; Zhang, Liang; Cope, Anne D.; Filliben, James J.; Hamid, Shahid

doi:10.1061/(asce)0733-9445(2004)130:11(1685)

Cited by 138 publications

(67 citation statements)

References 8 publications

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“…In contrast to empirical approaches, it was tried to construct deterministic models (e.g. Unanwa et al, 2000;Pinelli et al, 2004). However, these projects only refer to the US and need large amounts of specific building information that is not available on a large scale in Germany.…”

Section: Introductionmentioning

confidence: 99%

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Heneka

Hofherr

Ruck

et al. 2006

Nat. Hazards Earth Syst. Sci.

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Abstract. The derivation of probabilities of high wind speeds and the establishment of risk curves for storm damage is of prime importance in natural hazard risk analysis. Risk curves allow the assessment of damage being exceeded at a given level of probability.In this paper, a method for the assessment of winter storm damage risk is described in detail and applied to the German state of Baden-Württemberg. Based on meteorological observations of the years 1971-2000 and on damage information of 4 severe storm events, storm hazard and damage risk of residential buildings is calculated on the level of communities. For this purpose, highly resolved simulations of storm wind fields with the Karlsruher Atmospheric Mesoscale Model (KAMM) are performed and a storm damage model is developed.Risk curves including the quantification of the uncertainties are calculated for every community. Local differences of hazard and risk are presented in state-wide maps. An average annual winter storm damage to residential buildings of minimum 15 million Euro (reference year 2000) for BadenWürttemberg is expected.

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Section: Introductionmentioning

confidence: 99%

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Heneka

Hofherr

Ruck

et al. 2006

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Especial attention has been given to civil structures as they are critical for protection during the event and post-event recovery [149]. Pinelli et al [150] presented a prediction model for wind damage in residential structures. Assessment of risk for low-rise light-frame wood residential constructions is presented by Li and Ellingwood [151], where uncertainties are introduced in the reliability analysis.…”

Section: Hurricanesmentioning

confidence: 99%

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

2017

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Abstract. This article presents a state-of-the-art review of di erent methods, signal and image processing techniques, and statistical analyses used for prediction and assessment of natural disasters including earthquakes, tsunamis, volcanic eruptions, hurricanes, tornadoes, and oods. Application of the big data paradigm to the aforementioned natural disasters is also discussed. The research for increasingly more sophisticated computational models will continue to achieve more accurate predictions of various natural disasters.

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“…Ellingwood et al (2004) made limited comparisons between theoretical fragilities for roof panels and roof-to-wall connections and damage observations from Hurricane Andrew and observed qualitatively consistent results between the two methods, but noted that validation using empirical data was a challenge due to the complexity of the hurricane hazard as well as the wide variety of building configurations present in actual communities. Limited studies are also available as validation of hurricane catastrophe models (Pinelli et al, 2004;Federal Emergency Management Agency 2012), but currently no studies exist which develop empiricallybased fragility functions for residential structures considering tornado hazards.…”

Section: Introductionmentioning

confidence: 99%

Development of Empirically-Based Fragilities of Residential Damage in the 2011 Joplin, Missouri, Tornado

Roueche

Lombardo

Prevatt

2016

Geotechnical and Structural Engineering Congress 2016

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Performance-based engineering (PBE) is a methodology that requires specification on a range of performances or target reliabilities for structures of different importance. Information on these 'performance levels' require a probabilistic assessment of the potential factors that may influence a design, including information on the hazard, load, resistance, loss estimates, expert opinion and public perception. This paper describes one such probabilistic assessment in the development of empirically-based fragility functions for tornadoes using damage assessment data and a tornado wind field model for the 22 May 2011 Joplin, MO tornado. The damage assessment data was collected during field surveys of more than 1,240 structures in the aftermath of the tornado, using provisions of the Enhanced Fujita (EF) Scale to assess the damage. The wind field model was developed from the tree-fall patterns noted in the damage path of the tornado. Fragility functions were developed for the Degrees of Damage (DOD) associated with One-and Two-Family Residences in the EF Scale. The empiricallyderived fragility functions were progressive in nature, with median wind speeds varying from 33.6 m/s for initiation of visible damage to 85.2 m/s for complete destruction. These functions were compared to existing fragility functions for straightline winds to evaluate potential differences in failure mechanisms for structures exposed to tornadoes. Wind speeds associated with the median failure probability were used to estimate load factors, defined as the square of the ratio of the straightline wind speed to the tornado wind speed. Structures tended to fail at lower wind speeds in tornadoes than in straightline winds, with load factors between 1.32 and 1.51. A fragility assessment in the context of PBE naturally requires attribution and quantification of all uncertainties. Uncertainties in the both the damage and wind speed estimation in the development of fragilities are quantified and assessed using Monte Carlo methods. Preliminary results show variance in fragility parameters is higher for higher damage states but all damage states have relatively low coefficients of variation.

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Hurricane Damage Prediction Model for Residential Structures

Cited by 138 publications

References 8 publications

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

Development of Empirically-Based Fragilities of Residential Damage in the 2011 Joplin, Missouri, Tornado

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