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

A Study on Influential Factors on Building Damage in Kesennuma, Japan from the 2011 Great East Japan Tsunami

A number of buildings were damaged by the 2011 Great East Japan tsunami in the Tohoku area. The research objective is to determine the significant predictor variables of the level of building damage. This paper used detailed data on damaged buildings in Kesennuma City, Japan, collected by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT). The tested explanatory parameters included the inundation depth, number of floors, volume of the building, debris flow, structural material, and function of the building. Through multinomial logistic regression, the results found that the number of floors was significantly associated with the damage level; the inundation depth, structural material (reinforced concrete and masonry), and function of the building (commercial facility, transportation/storage facility, and public facility) were partially associated with the damage level. This study can contribute to academic research by assessing the contribution of different variables to observed damage data by applying statistical analysis, as well as the practical contribution of providing an examination of the predominant factors driving tsunami damage to buildings

Lessons Learned from the 2011 Great East Japan Tsunami: Performance of Tsunami Countermeasures, Coastal Buildings, and Tsunami Evacuation in Japan

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, non-structural (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

Cascading disasters triggered by tsunami hazards: A perspective for critical infrastructure resilience and disaster risk reduction

Although many studies have investigated relationships between tsunami characteristics and the impact on physical property and infrastructure, such information cannot explain how the damage to each object or type of infrastructure can trigger failures of other facilities. To understand these connections and the cascading impacts, this article reviewed several recent damaging tsunami events in Japan and Indonesia, including the 2004 Indian Ocean tsunami and the 2011 Great East Japan Earthquake and tsunami. A proposed cascading magnitude scale was applied to each tsunami event to determine and categorize causes, effects, and escalation points. Large tsunamis tend to be associated with earthquakes, liquefaction, and landslides that multiply the scale of impact. The main escalation points for tsunami related disasters were found to be failures of tsunami warnings, power plants, medical facilities, educational facilities, and infrastructure. From the perspectives of critical infrastructure resilience and disaster risk reduction, analysis of cascading impacts of multiple recent tsunami events could contribute to greater understanding of economic, political, and social impacts that stem from technical decisions regarding infrastructure management. Detailed examples of tsunami cases demonstrate the potential scale and extent of damage from cascading events, and by identifying the roles and examples of escalation points, disaster managers and decision-makers can better mitigate cascading impacts by targeting and preventing escalation points. However, more detailed investigation on tsunami characteristics and their impact on failures of each type of facility is still needed to develop tools to support decision-making for better emergency management to address short- and long-term social impacts

A proposed methodology for deriving tsunami fragility functions for buildings using optimum intensity measures

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 non-engineered (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 non-engineered 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 (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 cross-validation (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

Perceptions of the COVID-19 pandemic in Japan with respect to cultural, information, disaster and social issues

A questionnaire survey was distributed via the Internet to 600 respondents. Preliminary results revealed that most Japanese people regularly washed their hands and had low resistance to wearing masks even before the COVID-19 pandemic. Internet news was the most common source of information. Half of the respondents said they would “stay at home evacuation” if a disaster occurred during the COVID-19 pandemic, reflecting the strategy promoted to reduce crowding in evacuation shelters. If a state of emergency must be reinstated, one-third of respondents said they could bear it for a few months and another one-third for a few weeks