Combined Wind-Wave-Surge Hurricane-Induced Damage Prediction for Buildings

Masoomi, Hassan; Lindt, John W. van de; Ameri, Mohammad Reza; Trung, Q.; Webb, Bret M.

doi:10.1061/(asce)st.1943-541x.0002241

Cited by 34 publications

(15 citation statements)

References 30 publications

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“…Precise and not merely accurate estimates of hurricane-forced SWHs have the potential to enhance risk assessments and mitigation strategies as these systems make landfall or approach offshore structures (Hatzikyriakou and Lin, 2017;Marsooli and Lin, 2018;Masoomi et al, 2018;Guo et al, 2020;Song et al, 2020). This first section investigates the distribution of forecasted SWHs in comparison with observations for hurricanes Dorian, Sandy, and Igor.…”

Section: Histogram Analysismentioning

confidence: 99%

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Bethel

Sun

2021

Preprint

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Abstract. A Long Short-Term Memory (LSTM) neural network is proposed to predict hurricane-forced significant wave heights (SWH) in the Caribbean Sea (CS) based on a dataset of 20 CS, Gulf of Mexico, and Western Atlantic hurricane events collected from 10 buoys from 2010–2020. SWH nowcasting and forecasting are initiated using LSTM on 0-, 3-, 6-, 9-, and 12-hour horizons. Through examining study cases Hurricanes Dorian (2019), Sandy (2012), and Igor (2010), results illustrate that the model is well suited to forecast hurricane-forced wave heights. Forecasts are highly accurate with regard to observations. For example, Hurricane Dorian nowcasts had correlation (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 0.99, 0.16 m, and 2.6 %, respectively. Similarly, on the 3-, 6-, 9-, and 12-hour forecasts, results produced R (RMSE; MAPE) values of 0.95 (0.51 m; 7.99 %), 0.92 (0.74 m; 10.83 %), 0.85 (1 m; 13.13 %), and 0.84 (1.24 m; 14.82 %), respectively. However, the model also consistently over-predicted the maximum observed SWHs. To improve models results, additional research should be geared towards improving single-point LSTM neural network training datasets by considering hurricane track and identifying the hurricane quadrant in which buoy observations are made.

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Section: Histogram Analysismentioning

confidence: 99%

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Bethel

Sun

2021

Preprint

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“…The damage curve is an important tool in risk assessment science related to the vulnerability of structures (Pistrika and Jonkman, 2010;Englhardt et al, 2019). From the structural point of view, damage curves depend on the construction materials that buildings are made of (Huizinga, et al, 2017;Postacchini et al, 2019;Masoomi et al, 2019). Damage curves also depend on construction methods, codes, and building layout, including the distance between buildings (Suppasri et al, 2013;Jansen et al, 2020;Masoomi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…From the structural point of view, damage curves depend on the construction materials that buildings are made of (Huizinga, et al, 2017;Postacchini et al, 2019;Masoomi et al, 2019). Damage curves also depend on construction methods, codes, and building layout, including the distance between buildings (Suppasri et al, 2013;Jansen et al, 2020;Masoomi et al, 2019). The current paper focuses on one-two-storey masonry buildings under the effect of storm surge and wave forces produced by an extratropical storm in northwest France.…”

Section: Introductionmentioning

confidence: 99%

Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations

Loaiza

Bricker

Meynadier

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. The Delft3D hydrodynamic and wave model is used to hindcast the storm surge and waves that impacted La Rochelle, France, and the surrounding area (Aytré, Châtelaillon-Plage, Yves, Fouras, and Île de Ré) during storm Xynthia. These models are validated against tide and wave measurements. The models then estimate the footprint of flow depth, speed, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force at the locations of damaged buildings for which insurance claims data are available. Correlation of the hydrodynamic and wave results with the claims data generates building damage functions. These damage functions are shown to be sensitive to the topography data used in the simulation, as well as the hydrodynamic or wave forcing parameter chosen for the correlation. The most robust damage functions result from highly accurate topographic data and are correlated with water depth or total (flow plus wave) force.

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“…1 Damages attributable to these storms are mainly due to three factors, namely extreme wind, storm surge, and torrential rainfall (Bakkensen et al 2018;Park et al 2013;Lin et al 2010). The literature modelling the economic impact has, however, mainly focused on damages as a result of wind, and to a lesser extent storm surge (Emanuel 2005;Nordhaus 2006;Hu et al 2016; Baradaranshoraka et al 2017;Masoomi et al 2019;Hatzikyriakou and Lin 2018;Do et al 2020), due to the difficulties associated with large-scale flood modelling as a result of extreme precipitation (Murnane and Elsner 2012;Zhai and Jiang 2014). This is often justified on the grounds that wind is strongly correlated with rainfall during a TC and thus that wind exposure will capture damages due to rainfall as well.…”

Section: Introductionmentioning

confidence: 99%

Economic damages due to extreme precipitation during tropical storms: evidence from Jamaica

Collalti

Strobl

2021

Nat Hazards

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This study investigates economic damage risk due to extreme rainfall during tropical storms in Jamaica. To this end, remote sensing precipitation data are linked to regional damage data for five storms. Extreme value modelling of precipitation is combined with an estimated damage function and satellite-derived nightlight intensity to estimate local risk in monetary terms. The results show that variation in maximum rainfall during a storm significantly contributes to parish level damages even after controlling for local wind speed. For instance, the damage risk for a 20 year rainfall event in Jamaica is estimated to be at least 238 million USD, i.e. about 1.5% of Jamaica’s yearly GDP.

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Combined Wind-Wave-Surge Hurricane-Induced Damage Prediction for Buildings

Cited by 34 publications

References 30 publications

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Forecasting Hurricane-forced Significant Wave Heights using the Long Short-Term Memory Network in the Caribbean Sea

Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations

Economic damages due to extreme precipitation during tropical storms: evidence from Jamaica

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