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

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.