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

Abstract

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.

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

“…Typically, fragility functions are derived by using linear least squares regression, assuming that the response to be modeled follows a normal or lognormal distribution, and by grouping or re-regrouping the data into bins of tsunami intensity (e.g., Suppasri et al 2011Suppasri et al , 2012a Nevertheless, it was shown by Charvet et al (2013b) and Charvet et al (2014a, b) that considerable uncertainty was introduced in the damage predictions when such methods are used as a tool to analyze building damage data. The results from Charvet et al (2014a) also indicate that highly aggregated databases (i.e., when observations are grouped into a range of tsunami IM, and/or grouped over a wide range of geographical locations) result in a loss of information, potentially yielding variations which cannot be explained by the model. The building damage analysis following the 2009 Samoa tsunami by Reese et al (2011) was the first study in the tsunami engineering field which derived fragility functions using a more adequate statistical approach, namely logistic regression, assuming the damage response to follow a binomial distribution (i.e., damaged/not damaged).…”

Empirical fragility analysis of building damage caused by the 2011 Great East Japan tsunami in Ishinomaki city using ordinal regression, and influence of key geographical features

“…Indeed, this procedure does not recognise that some bins have a higher number of buildings than others, and cannot deal with the bins which do not contain any damaged buildings, or only contain damaged buildings (due to the fact the inverse normal distribution function does not converge for probabilities of 0 or 1). In addition, depending on the level of data aggregation significant information may not be captured by the model (Charvet et al 2014). The building damage analysis conducted by Reese et al (2011) was the first study in the tsunami engineering field which implemented more realistic stochastic models to represent damage probability.…”

Building Damage Assessment Using Scenario Based Tsunami Numerical Analysis and Fragility Curves

“…These curves are based on fragility functions developed for Kesennuma (Japan), which was extensively damaged by the 2011 tsunami. The use of ordinal regression analysis in [9] addresses the issues of damage uncertainty associated with linear regression and the shortcoming of logistic regression in that it does not utilize all the damage information [18]. …”

“…[33,34] developed fragility curves for areas affected by the 2004 Indian Ocean tsunami in Sri Lanka, and [35] estimated fragility curves for Banda Aceh and South Java in Indonesia. Most of the existing work on fragility curves was performed in Japan after the 2011 tsunami [4,9,18,25]. Based on a comparison of construction practices and building typologies, we choose the fragility curves presented in [9] to estimate structure damage in Imwon Port.…”