Developing tsunami fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand

Abstract

Abstract. The 2004 Indian Ocean tsunami damaged and destroyed numerous buildings and houses in Thailand. Estimation of tsunami impact to buildings from this event and evaluation of the potential risks are important but still in progress. The tsunami fragility curve is a function used to estimate the structural fragility against tsunami hazards. This study was undertaken to develop fragility curves using visual inspection of high-resolution satellite images (IKONOS) taken before and after tsunami events to classify whether the buildings were destroyed or not based on the remaining roof. Then, a tsunami inundation model is created to reconstruct the tsunami features such as inundation depth, current velocity, and hydrodynamic force of the event. It is assumed that the fragility curves are expressed as normal or lognormal distribution functions and the estimation of the median and log-standard deviation is performed using least square fitting. From the results, the developed fragility curves for different types of building materials (mixed type, reinforced concrete and wood) show consistent performance in damage probability and when compared to the existing curves for other locations.

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

“…This issue is addressed in some studies by re-grouping buildings into bins with approximately equal number of buildings, resulting in some bins corresponding to a rather wide range of intensity measure levels. For instance, Suppasri et al (2011) formed even bins of 50 buildings (Phuket) and 100 buildings (Phang Nga); Suppasri et al (2012) formed bins of 15 buildings in the study areas of Sendai and Ishinomaki. However, this re-grouping can affect the shape of the fragility curve especially at the tails.…”

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

“…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.…”

“…Fragility functions are derived by applying regression analysis techniques to the classified observations, using damage state as response variable and the chosen IM (most commonly the tsunami flow depth-e.g., Suppasri et al 2013a, b; in some cases numerically estimated flow velocities, or analytically estimated hydrodynamic forces-e.g., Suppasri et al 2012b) as the explanatory variable. 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.…”

Risk factors and perceived restoration in a town destroyed by the 2010 Chile tsunami

“…(1) was K = 1.09 with a standard deviation from Eq. (2) of κ = 0.12, which is considered acceptable (Suppasri et al, 2011). Figure 3c shows the tsunami wave form obtained from the synthetic tide gauge.…”

“…Furthermore, a virtual tide gauge on the Dichato beachfront was defined to obtain arrival times of different tsunami waves. The validation of the numerical simulation was performed using the root mean square error and the parameters K and κ proposed by Aida (1978), cited by Suppasri et al (2011) and given below in Eqs. (1) and (2).…”