Estimating Tsunami-Induced Building Damage through Fragility Functions: Critical Review and Research Needs

Tsunami damage, fragility, and vulnerability functions are statistical models that provide an estimate of expected damage or losses due to tsunami. They allow for quantification of risk, and so are a vital component of catastrophe models used for human and financial loss estimation, and for land-use and emergency planning. This paper collates and reviews the currently available tsunami fragility functions in order to highlight the current limitations, outline significant advances in this field, make recommendations for model derivation, and propose key areas for further research. Existing functions are first presented, and then key issues are identified in the current literature for each of the model components: building damage data (the response variable of the statistical model), tsunami intensity data (the explanatory variable), and the statistical model that links the two. Finally, recommendations are made regarding areas for future research and current best practices in deriving tsunami fragility functions (see Discussion, Recommendations, and Future Research). The information presented in this paper may be used to assess the quality of current estimations (both based on the quality of the data, and the quality of the models and methods adopted) and to adopt best practice when developing new fragility functions

Implications of the 2011 Great East Japan Tsunami on sea defence design

After the 2004 Boxing Day tsunami, much of the world's effort to defend against tsunami concentrated on tsunami warning and evacuation. The 2011 Great East Japan Earthquake and Tsunami led to direct and indirect losses as well as the deaths of many vulnerable members of Japan's coastal communities. This event has resulted in Japan rethinking and revising its design codes for sea defence structures. The new guidance emerging from this process is a valuable resource for other countries re-evaluating their own current mitigation strategies and this paper presents details of this process. The paper starts with the history of sea defence design standards in Japan and explains the process of revision of design guidelines since 2011. Examples of sea defences that failed and have since been rebuilt, observed during the two Earthquake Engineering Field Investigation Team (EEFIT) missions of 2011 and 2013, are also presented. The paper concludes with a discussion of international approaches and their application to nuclear power stations in Japan and the UK

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

Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and building stock. Therefore, this paper outlines an approach to the creation of event-specific vulnerability curves, using Bayesian statistics (i.e., the zero-one inflated beta distribution) to update a pre-existing vulnerability curve (i.e., the prior) with observed impact data derived from social media. The approach is applied in a case study of Hurricane Dorian, which hit the Bahamas in September 2019. We analysed footage shot predominantly from unmanned aerial vehicles (UAVs) and other airborne vehicles posted on YouTube in the first 10 days after the disaster. Due to its Bayesian nature, the approach can be used regardless of the amount of data available as it balances the contribution of the prior and the observations

Tsunami design procedures for engineered buildings: A critical review

Tsunamis have the potential to cause enormous loss of life and socio-economic impacts on coastal communities. Central to tsunami risk mitigation is the protection of critical infrastructure and evacuation-designated buildings, which are often necessarily located within tsunami inundation zones. As such, these must be designed to withstand and remain fully or partially operational after a tsunami. Guidance documents for tsunami design of buildings exist in the USA and Japan, including the recent release of the US ASCE 7 chapter 6 on tsunami loads and effects. This paper outlines the key engineering principles of tsunami design of buildings, summarises and compares how these principles are addressed by US and Japanese standards, and outlines considerations not yet covered

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.</jats:p

Lessons Learnt From the 2009 Padang Indonesia, 2011 Tōhoku Japan and 2016 Muisne Ecuador Earthquakes

This paper presents the observations during the UK Earthquake Engineering Field Investigation Team (EEFIT)'s post-earthquake reconnaissance missions to the September 20, 2009 Padang (Mw7.6), March 11, 2011 Tōhoku (Mw9.0) and April 16, 2016 Muisne (Mw7.8) earthquakes. The performance of buildings and geotechnical structures within the affected regions were investigated to gain insights on their design and construction deficiencies. Findings on these damages observed are compared along with the characteristics of the earthquake and nature of building codes in these countries. They include building damages attributed to a combination of structural resonance, deficiencies in reinforcement detailing, vulnerability to soft story collapse, ground settlement, soil liquefaction, and landslides. It was demonstrated that buildings which were severely damaged had natural building frequencies coinciding with the dominant frequencies of the ground shaking. The locations of damage of several such buildings showed insufficient confining reinforcements and lapping of stirrup links. Soft story collapses were also observed in the three earthquakes, although many were attributed to old building codes that were less effective. In areas affected by the Muisne earthquake, soft story collapses were mainly found at mid height of the building rather than at the ground floor as observed in the Padang and Tōhoku earthquakes, likely due to extension of building long after the bottom floors were completed. Such extension of building can either lead to local reduction in capacity due to weaker concrete-rebar bonding, possibly insufficient lapping of reinforcement, as well as increased axial loads. In the aspect of geotechnical failure, foundations of buildings found on piles performed reasonably well, except for areas affected by soil liquefaction. Landslides occurred following these earthquakes led to large concentration of casualties and property losses, motivating the EEFIT teams to invest efforts in hazard mapping and ground-truthing exercises using satellite images at Padang and Muisne earthquakes respectively. Such geospatial tools applied in these three earthquakes were reviewed and demonstrated to be capable of identifying landslide sites and producing reliable landslide hazard map

Investigation of the Effect of Debris-Induced Damage for Constructing Tsunami Fragility Curves for Buildings

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. Building damage due to tsunamis can occur due to fluid effects (e.g. drag) and debris impact, two effects which have different implications for building damage levels and mechanisms. However, existing studies often pool all available damage data for a location regardless of whether damage was caused by fluid or debris effects, and so it is not clear whether the inclusion of debris-induced damage introduces bias in existing fragility curves. This paper uses a detailed disaggregated damage dataset from the 2011 Great East Japan Earthquake and Tsunami together with several advanced statistical methods in order to identify the effect that debris-induced damage has on fragility function derivation. Buildings are identified which are most likely to have sustained significant debris damage, based on the proportion of nearby buildings which have been designated as “washed away” in their post-tsunami survey. Fragility curves are then constructed for observed inundation depth and simulated force, and fragility curves with/without debris impact are compared for each damage state. Finally complex models which include all buildings and additional parameters corresponding to debris impact are considered. The influence of debris model parameters on determining building damage was shown to be significant for all but the lowest damage state (“minor damage”), and more complex fragility functions which incorporate debris model parameters were shown to have a statistically significant better fit to the observed damage data than models which omitted debris information