Abstract
We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu.
Highlights
Tsunami fragility functions (TFFs) are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific value of tsunami intensity measure or another engineering demand parameter
TFFs from (i) posttsunami field survey data, (ii) Copernicus Emergency Management System (EMS) visual damage interpretation data and a tsunami inundation depth surface constructed from the posttsunami field measurements, and (iii) machine learning-based remote sensing classification of building damage data combined with the tsunami inundation depth surface
For this tsunami damage observed after the 2018 Sulawesi event in Indonesia, the field survey data (FS) TFF exhibits a similar shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, with a slightly lower damage probability between 0-to-5-m inundation depth
Summary
Tsunami fragility functions (TFFs) are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific value of tsunami intensity measure or another engineering demand parameter. To develop a TFF, it is necessary to compile a set of damage classification data samples and to correlate these with the tsunami intensity measures for sample locations following a particular statistical model. We will concentrate only on empirical TFFs. In the effort to develop TFFs, several field teams may measure inundation depths and record later damage levels at affected asset locations. In the effort to develop TFFs, several field teams may measure inundation depths and record later damage levels at affected asset locations This activity is time-consuming and may place research teams at risk of harm in tsunami affected areas.
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