Developing Fragility Functions for Tsunami Damage Estimation Using Numerical Model and Post-Tsunami Data from Banda Aceh, Indonesia

Koshimura, Shunichi; Oie, Takayuki; Yanagisawa, Hideaki; Imamura, Fumihiko

doi:10.1142/s0578563409002004

Cited by 316 publications

(269 citation statements)

References 29 publications

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“…Damage levels are typically defined prior to a post-tsunami survey by engineering teams and describe the condition of the affected structure, from zero damage to complete failure, thus forming a damage scale. Such scale is then used in combination with tsunami flow depth measurements (Ruangrassamee et al 2006;Mas et al 2012;Suppasri et al 2012aSuppasri et al , 2013a or results from numerical simulations (Koshimura et al 2009;Suppasri et al 2011Suppasri et al , 2012b in order to classify the surveyed buildings according to their damage state and a corresponding IM. The first column of Table 1 presents the damage scale defined by the Ministry of Land, Infrastructure, Transport and Tourism in Japan (MLIT) for the survey of the buildings affected by the Great East Japan tsunami that struck the country on March, 11, 2011.…”

Section: Introductionmentioning

confidence: 99%

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

et al. 2015

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The recent losses caused by the unprecedented 2011 Great East Japan Tsunami disaster have stimulated further research efforts, notably in the mechanisms and probabilistic determination of tsunami-induced damage, in order to provide the necessary information for future risk assessment and mitigation. The stochastic approach typically adopts fragility functions, which express the probability that a building will reach or exceed a predefined damage level usually for one, sometimes several measures of tsunami intensity. However, improvements in the derivation of fragility functions are still needed in order to yield reliable predictions of tsunami damage to buildings. In particular, extensive disaggregated databases, as well as measures of tsunami intensity beyond the commonly used tsunami flow depth should be used to potentially capture variations in the data which have not been explained by previous models. This study proposes to derive fragility functions with additional intensity measures for the city of Kesennuma, which was extensively damaged during the 2011 tsunami and for which a large and disaggregated dataset of building damage is available. In addition to the surveyed tsunami flow depth, the numerically estimated flow velocities as well as a binary indicator of debris impact are included in the model and used simultaneously to estimate building damage probabilities. Following the recently proposed methodology for fragility estimation based on generalized linear models, which overcomes the shortcomings of classic linear regression in fragility analyses, ordinal regression is applied and the reliability of the model estimates is assessed using a proposed penalized accuracy measure, more suitable than the traditional classification error rate for ordinal models. In order to assess the predictive power of the model, penalized accuracy is estimated through a repeated tenfold cross-validation scheme. For the first time, multivariate tsunami fragility functions are derived and represented in the 123Nat Hazards (2015) 79:2073-2099 DOI 10.1007/s11069-015-1947 form of fragility surfaces. The results show that the model is able to predict tsunami damage with satisfactory predictive accuracy and that debris impact is a crucial factor in the determination of building collapse probabilities.

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Section: Introductionmentioning

confidence: 99%

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

et al. 2015

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“…Peiris 2006) or satellite data (e.g. Koshimura et al 2009). A statistical model consists of a random and a systematic component.…”

Section: Introductionmentioning

confidence: 99%

“…The systematic component typically represents the probability that a damage state is reached or exceeded given the tsunami intensity, and is called the fragility curve. Fragility curves are key components in seismic and tsunami loss estimation and have been constructed following the 2004 tsunami in Indonesia (Koshimura et al 2009), in Sri Lanka (Peiris 2006;Murao and Nakazato 2010) and Thailand , as well as after the 2011 Great East Japan tsunami (Suppasri et al , 2013a.…”

Section: Introductionmentioning

confidence: 99%

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Empirical fragility assessment of buildings affected by the 2011 Great East Japan tsunami using improved statistical models

et al. 2014

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Tsunamis are destructive natural phenomena which cause extensive damage to the built environment, affecting the livelihoods and economy of the impacted nations. This has been demonstrated by the tragic events of the Indian Ocean tsunami in 2004, or the Great East Japan tsunami in 2011. Following such events, a few studies have attempted to assess the fragility of the existing building inventory by constructing empirical stochastic functions, which relate the damage to a measure of tsunami intensity. However, these studies typically fit a linear statistical model to the available damage data, which are aggregated in bins of similar levels of tsunami intensity. This procedure, however, cannot deal well with aggregated data, low and high damage probabilities, nor does it result in the most realistic representation of the tsunami-induced damage. Deviating from this trend, the present study adopts the more realistic generalised linear models which address the aforementioned disadvantages. The proposed models are fitted to the damage database, containing 178,448 buildings surveyed in the aftermath of the 2011 Japanese tsunami, provided by the Ministry of Land, Infrastructure Transport and Tourism in Japan. In line with the results obtained in previous studies, the fragility curves show that wooden buildings (the dominant construction type in Japan) are the least resistant against tsunami loading. The diagnostics show that taking into account both the building's construction type and the tsunami flow depth is crucial to the quality of the damage estimation and that these two variables do not act independently. In addition, the diagnostics reveal that tsunami flow depth estimates low levels of damage reasonably well; however, it is not the most representative measure of intensity of the tsunami for high damage states (especially structural damage). Further research using disaggregated damage data and additional explanatory variables is required in order to obtain reliable model estimations of building damage probability.

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“…For tsunami inundation, the intensity measure may be flood depth, flow speed, or momentum flux. Following the 2004 tsunami, researchers relied on before and after aerial photos to estimate damage levels to buildings coupled with corresponding hydrodynamics or numerical simulations and some field observations to estimate tsunami fragility curves (Koshimura, et al, 2009;. After the 2011 tsunami, various research groups were organized to estimate the specific damage levels of buildings and the corresponding maximum flow depths or runups with field surveys.…”

Section: Damage Estimation Using Fragility Curvesmentioning

confidence: 99%

Tsunami Inundation Modeling: Sensitivity of Velocity and Momentum Flux to Bottom Friction With Application to Building Damage at Seaside, Oregon

Park

Wiebe

Cox

et al. 2014

Int. Conf. Coastal. Eng.

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We examine the sensitivity of three different tsunami inundation numerical models using various friction terms. We use the model output to examine the probabilistic damage levels using fragility curves applied over a community scale and resolved at the scale of individual tax lots for Seaside, OR. With this work, we estimate the inundation hazard using the "500 year" tsunami originating from a Cascadia Subduction Zone earthquake and then compare the maximum surface elevation, velocity, and momentum flux results across the three models. We find a larger variation in the velocities and momentum fluxes when varying model types and friction coefficients; surface elevation variations are not as large. We also find that absolute velocity and momentum flux are more sensitive to friction factors rather than model type, while surface elevation varies with model type. For the fragility curve analysis, we consider flow depth, velocity, and momentum flux as the intensity measure to estimate the probability of a certain damage level based on the known structure type and characteristic tsunami intensity. We examine the sensitivity of damage levels to various fragility curves, using different intensity measures, and we find that velocity and momentum flux curves provide a more realistic estimate of damage.

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Developing Fragility Functions for Tsunami Damage Estimation Using Numerical Model and Post-Tsunami Data from Banda Aceh, Indonesia

Cited by 316 publications

References 29 publications

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

Empirical fragility assessment of buildings affected by the 2011 Great East Japan tsunami using improved statistical models

Tsunami Inundation Modeling: Sensitivity of Velocity and Momentum Flux to Bottom Friction With Application to Building Damage at Seaside, Oregon

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