A large amount of buildings was damaged or destroyed by the 2011 Great East Japan tsunami. Numerous field surveys were conducted in order to collect the tsunami inundation extents and building damage data in the affected areas. Therefore, this event provides us with one of the most complete data set among tsunami events in history. In this study, fragility functions are derived using data provided by the Ministry of Land, Infrastructure and Transportation of Japan, with more than 250,000 structures surveyed. The set of data has details on damage level, structural material, number of stories per building and location (town). This information is crucial to the understanding of the causes of building damage, as differences in structural characteristics and building location can be taken into

Abstract-In 2011, Japan was hit by a tsunami that was generated by the greatest earthquake in its history. The first tsunami warning was announced 3 min after the earthquake, as is normal, but failed to estimate the actual tsunami height. Most of the structural countermeasures were not designed for the huge tsunami that was generated by the magnitude M = 9.0 earthquake; as a result, many were destroyed and did not stop the tsunami. These structures included breakwaters, seawalls, water gates, and control forests. In this paper we discuss the performance of these countermeasures, and the mechanisms by which they were damaged; we also discuss damage to residential houses, commercial and public buildings, and evacuation buildings. Some topics regarding tsunami awareness and mitigation are discussed. The failures of structural defenses are a reminder that structural (hard) measures alone were not sufficient to protect people and buildings from a major disaster such as this. These defenses might be able to reduce the impact but should be designed so that they can survive even if the tsunami flows over them. Coastal residents should also understand the function and limit of the hard measures. For this purpose, nonstructural (soft) measures, for example experience and awareness, are very important for promoting rapid evacuation in the event of a tsunami. An adequate communication system for tsunami warning messages and more evacuation shelters with evacuation routes in good condition might support a safe evacuation process. The combination of both hard and soft measures is very important for reducing the loss caused by a major tsunami. This tsunami has taught us that natural disasters can occur repeatedly and that their scale is sometimes larger than expected.

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.

The 2011 Great East Japan Earthquake/Tsunami was a magnitude 9.0 Mw event that destroyed most structural tsunami countermeasures. However, approximately 90% of the estimated population at risk from the tsunami survived due to rapid evacuation to higher ground or inland. In this paper, we introduce an evacuation model integrated with a numerical simulation of a tsunami and a casualty estimation evaluation. The model was developed in Netlogo, a multi-agent programming language and modeling environment for simulating complex phenomena. GIS data are used as spatial input information for road and shelter locations. Tsunami departure curves are used as the start time for agents deciding to evacuate in the model. Pedestrians and car drivers decide their own goals and search for a suitable route through algorithms that are also used in the video game and artificial intelligence fields. Bottleneck identification, shelter demand, and casualty estimation are some of the applications of the simulator. A case study of the model is presented for the village of Arahama in the Sendai plain area of Miyagi Prefecture in Japan. A stochastic simulation with 1,000 repetitions of evacuation resulted in a mean of 82.1% (SD=3.0%) of the population evacuated, including a total average of 498 agents evacuating to a multi-story shelter. The results agree with the reported outcome of 90% evacuation and 520 sheltered evacuees in the event. The proposed model shows the capability of exploring individual parameters and outcomes. The model allows observation of the behavior of individuals in the complex process of tsunami evacuation. This tool is important for the future evaluation of evacuation feasibility and shelter demand analysis.

Abstract. On 27 February 2010, a megathrust earthquake of M w = 8.8 generated a destructive tsunami in Chile. It struck not only Chilean coast but propagated all the way to Japan. After the event occurred, the post-tsunami survey team was assembled, funded by the Japan Science and Technology Agency (JST), to survey the area severely affected by the tsunami. The tsunami damaged and destroyed numerous houses, especially in the town of Dichato. In order to estimate the structural fragility against tsunami hazard in this area, tsunami fragility curves were developed. Surveyed data of inundation depth and visual inspection of satellite images of Dichato were used to classify the damage to housing. A practical method suitable when there are limitations on available data for numerical simulation or damage evaluation from surveys is presented here. This study is the first application of tsunami fragility curves on the South American Pacific coast and it might be of practical use for communities with similar characteristics along the west Pacific coast. The proposed curve suggests that structures in Dichato will be severely damaged -with a 68 % probability -already at 2 m tsunami inundation depth.

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Tsunami fragility curves are statistical models which form a key component of tsunami risk models, as they provide a probabilistic link between a tsunami intensity measure (TIM) and building damage. Existing studies apply different TIMs (e.g. depth, velocity, force etc.) with conflicting recommendations of which to use. This paper presents a rigorous methodology using advanced statistical methods for the selection of the optimal TIM for fragility function derivation for any given dataset. This methodology is demonstrated using a unique, detailed, disaggregated damage dataset from the 2011 Great East Japan earthquake and tsunami (total 67,125 buildings), identifying the optimum TIM for describing observed damage for the case study locations. This paper first presents the proposed methodology, which is broken into three steps: (1) exploratory analysis, (2) statistical model selection and trend analysis and (3) comparison and selection of TIMs. The case study dataset is then presented, and the methodology is then applied to this dataset. In Step 1, exploratory analysis on the case study dataset suggests that fragility curves should be constructed for the sub-categories of engineered (RC and steel) and nonengineered (wood and masonry) construction materials. It is shown that the exclusion of buildings of unknown construction material (common practice in existing studies) may introduce bias in the results; hence, these buildings are estimated as engineered or nonengineered through use of multiple imputation (MI) techniques. In Step 2, a sensitivity analysis of several statistical methods for fragility curve derivation is conducted in order to select multiple statistical models with which to conduct further exploratory analysis and the TIM comparison (to draw conclusions which are non-model-specific). Methods of data aggregation and ordinary least squares parameter estimation (both used in existing studies) are rejected as they are quantitatively shown to reduce fragility curve accuracy and increase uncertainty. Partially ordered probit models and generalised additive models 123Nat Hazards (2016) 84:1257-1285 DOI 10.1007/s11069-016-2485 (GAMs) are selected for the TIM comparison of Step 3. In Step 3, fragility curves are then constructed for a number of TIMs, obtained from numerical simulation of the tsunami inundation of the 2011 GEJE. These fragility curves are compared using K-fold crossvalidation (KFCV), and it is found that for the case study dataset a force-based measure that considers different flow regimes (indicated by Froude number) proves the most efficient TIM. It is recommended that the methodology proposed in this paper be applied for defining future fragility functions based on optimum TIMs. With the introduction of several concepts novel to the field of fragility assessment (MI, GAMs, KFCV for model optimisation and comparison), this study has significant implications for the future generation of empirical and analytical fragility functions.