Tsunami Fragility Functions Research Articles

Beyond tsunami fragility functions: experimental assessment for building damage estimation

Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a “disaster signature” with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better.

A comparative analysis of empirical and analytical tsunami fragility functions for buildings in Tumaco, Colombia

The Pacific coast of Colombia has witnessed several tsunami events in the last century. However, the physical damage to structures at the time was not recorded, even when these events have brought overwhelming destruction to coastal communities. This lack of data increases uncertainties when assessing future tsunami impacts. Nonetheless, other regions of the world, which have been also affected by tsunami, have collected damage information that has been used to build empirical tsunami fragility functions. Based on a demand parameter, such as the tsunami inundation depth, a fragility function expresses the probability of building damage. Hence, regions with no recent tsunami damage cannot develop empirical fragility functions since post-disaster damage survey data is not available. One alternative for these regions is to develop analytical fragility functions out of computational nonlinear structural analysis. Reinforced concrete buildings and palafitte houses are selected to perform a comparative analysis of collapse damage assessment expressed in empirical and analytical fragility functions.

Multi-Hazard Portfolio Loss Estimation for Time-Dependent Shaking and Tsunami Hazards

Megathrust subduction earthquakes generate intense ground shaking and massive tsunami waves, posing major threat to coastal communities. The occurrence of such devastating seismic events is uncertain and depends on their recurrence characteristics (e.g. inter-arrival time distribution and parameters) as well as elapsed time since the last major event. Current standard probabilistic loss models for earthquakes and tsunamis are based on a time-independent Poisson process and uniform earthquake slip distribution. Thereby, considerations of more realistic time-dependent earthquake occurrence and heterogeneous earthquake slip distribution are necessary. This study presents an innovative computational framework for conducting a time-dependent multi-hazard loss estimation of a building portfolio subjected to megathrust subduction earthquakes and tsunamis. The earthquake occurrence is represented by a set of multiple renewal models, which are implemented using a logic-tree approach, whereas earthquake rupture characterization is based on stochastic source models with variable fault geometry and heterogeneous slip distribution. By integrating these hazard components with seismic and tsunami fragility functions, multi-hazard loss potential for a coastal community can be evaluated quantitatively by considering different possibilities of earthquake recurrence and rupture characteristics. To demonstrate the implementation of the developed time-dependent multi-hazard loss model, the Tohoku region of Japan is considered.

Vulnerability of Florida residential structures to hurricane induced coastal flood

This paper presents a methodology to develop hurricane induced coastal flood vulnerability functions for residential construction based on empirical tsunami fragility functions. A force equivalency mapping procedure first transforms the tsunami fragility functions into coastal flood fragility functions. Following the quantification of the damage states and the incorporation of repair costs, the coastal flood fragility functions translate into coastal flood vulnerability functions. Insurance claims data from the National Flood Insurance Program and vulnerability functions independently derived by the US Army Corps of Engineers are employed to validate single-story on-grade timber and reinforced masonry structure model outputs. The limitations of the model and future developments are discussed.

Tsunami Fragility Functions for Road and Utility Pole Assets Using Field Survey and Remotely Sensed Data from the 2018 Sulawesi Tsunami, Palu, Indonesia

The 2018 Sulawesi tsunami caused widespread impacts in Palu City, Indonesia, including to components of infrastructure lifeline networks. Lifeline networks are key to the operation of society and are particularly crucial during post-disaster relief and recovery efforts. Understanding their vulnerability to tsunami hazards is important for disaster risk reduction, but is an understudied topic. This study uses field survey and remotely sensed data to develop a single dataset, used to create tsunami fragility functions for road and utility pole assets in Palu. Tsunami inundation depths were estimated at component locations from a spatial interpolation of field measured flow depths and wave run-up. Component attributes and geometries exposed to tsunami inundation were compiled from both field surveys and remote sensing on satellite imagery and ‘street view’ imagery, which included component construction material, capacity (roads) and height (poles). Roads demonstrate a 0.16 probability of exceeding complete damage at 2 m inundation depth, while utility poles see a 0.47 probability. The probability of exceeding complete damage at 2 m inundation depth for concrete, asphalt, collector and local roads is 0.34, 0.17, 0.19 and 0.13 respectively, and for concrete, steel, steel 5 m height utility poles is 0.42, 0.48, 0.49 and 0.47 respectively. When comparing the synthesised tsunami fragility functions to those from other global events, Palu roads were more vulnerable at 2 m inundation depth (0.16) compared to roads exposed to the 2011 Tohoku tsunami in Japan (0.06) and 2015 Illapel tsunami in Chile (0.05).

Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami

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.

A comparative study of empirical and analytical fragility functions for the assessment of tsunami building damage in Tumaco, Colombia

ABSTRACT A fragility function shows the building damage likelihood based on a demand parameter such as the tsunami flow depth. While empirical fragility functions have been developed by using postdisaster damage survey data, regions with no recent tsunami damage cannot develop these estimations. Alternatively, analytical fragility functions could be developed to fill this gap. Along the Colombian Pacific coast, several tsunami events have occurred; however, the aftermath resulting from the structure physical damage was not recorded. Fortunately, other regions of the have collected tsunami damage information, which has been analyzed and represented in terms of empirical fragility functions. This paper provides a comparative study of empirical and analytical tsunami fragility functions. It is focused on the analytical fragility functions developed for typical structures in Tumaco, Colombia and on empirical fragility curves taken from damage survey data of recent tsunami events. The differences in the assessments of the damage produced by the two methods in the case of a two-story reinforced concrete building in Tumaco is discussed herein. As a result of the comparison, it was shown that the structures in Tumaco have higher collapse probabilities at lower flow depths than structures in other places along the Pacific and Indian Oceans.

Analytical tsunami fragility curves for seaport RC buildings and steel light frame warehouses

Harbours are crucial assets for the sustainment and development of human activities. The recent devastating tsunami events as well as the increasing number of people, structures and economic activities being exposed to tsunami hazards revealed the need for the estimation of the effects of tsunami wave on seaport structures. However, only a limited number of tools to estimate the potential impacts of tsunami are available until now. This study aims at developing analytical tsunami fragility functions for some representative typologies of seaport structures in Greece. In particular, low-code moment resisting frame (MRF) and dual reinforced concrete (RC) buildings of various heights, and a typical warehouse are considered in the analysis. A numerical investigation is performed considering different combinations of tsunami loads based on FEMA P646 (2008) [1] recommendations for gradually increasing tsunami inundation depths and for the various structure typologies. To minimize the uncertainties related to the definition of damage limit states, tsunami nonlinear static analyses are performed and appropriate tsunami capacity curves are derived for the considered structures. Structural limit states are defined on tsunami capacity curves in terms of threshold values of material strain. For the complete damage state, shear failure is also considered, since the collapse of structures may be caused by the occurrence of either a flexural or shear failure in structural components. Fragility curves are numerically calculated for the different damage states using nonlinear regression analysis. They could be used within a probabilistic risk assessment framework to assess the vulnerability of low-code RC buildings and typical warehouses exposed to tsunami hazard along European-Mediterranean and other regions of similar facilities worldwide.

Developing fragility functions for aquaculture rafts and eelgrass in the case of the 2011 Great East Japan tsunami

Abstract. Since the two devastating tsunamis in 2004 (Indian Ocean) and 2011 (Great East Japan), new findings have emerged on the relationship between tsunami characteristics and damage in terms of fragility functions. Human loss and damage to buildings and infrastructures are the primary target of recovery and reconstruction; thus, such relationships for offshore properties and marine ecosystems remain unclear. To overcome this lack of knowledge, this study used the available data from two possible target areas (Mangokuura Lake and Matsushima Bay) from the 2011 Japan tsunami. This study has three main components: (1) reproduction of the 2011 tsunami, (2) damage investigation, and (3) fragility function development. First, the source models of the 2011 tsunami were verified and adjusted to reproduce the tsunami characteristics in the target areas. Second, the damage ratio (complete damage) of the aquaculture raft and eelgrass was investigated using satellite images taken before and after the 2011 tsunami through visual inspection and binarization. Third, the tsunami fragility functions were developed using the relationship between the simulated tsunami characteristics and the estimated damage ratio. Based on the statistical analysis results, fragility functions were developed for Mangokuura Lake, and the flow velocity was the main contributor to the damage instead of the wave amplitude. For example, the damage ratio above 0.9 was found to be equal to the maximum flow velocities of 1.3 m s−1 (aquaculture raft) and 3.0 m s−1 (eelgrass). This finding is consistent with the previously proposed damage criterion of 1 m s−1 for the aquaculture raft. This study is the first step in the development of damage assessment and planning for marine products and environmental factors to mitigate the effects of future tsunamis.

Development of Physics-Based Tsunami Fragility Functions Considering Structural Member Failures

A probabilistic framework is presented for the development of physics and simulation-based parametrized tsunami fragility functions for structures accounting for structural member failures. The proposed framework is general and accounts for material and geometric sources of uncertainty and makes use of nonlinear finite-element structural models and the first-order second-moment (FOSM) reliability method. The application of the framework is illustrated with the development of parametrized fragility functions for an example reinforced concrete moment frame building designed to recent United States codes. Results indicate that explicit consideration of structural member failures is of paramount importance because the fragility functions based on global failure criteria that do not account for member failures tend to overpredict damage state capacities. Among the several sources of uncertainty considered, breakaway openings in the building are the dominant contributor to the uncertainty in the structural capacity. In addition, the estimation efficiency of several scalar and vector-valued intensity measures as predictors of structural damage is evaluated using the logistic regression method. The intensity measures considered consist of inundation depth, flow velocity, specific momentum flux, kinematic moment of specific momentum flux, and their interactions. The estimation efficiency of vector-valued intensity measures is found to be higher than that of scalar intensity measures. Among the scalar intensity measures analyzed, those that combine information of inundation depth and flow velocity are identified to be the most efficient predictors of structural damage, and therefore are considered to be the preferred measures to characterize the intensity of tsunami hazards for practical applications.

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

Tsunami damage, fragility and vulnerability functions are statistical models which 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 which links the two. Finally, recommendations are made regarding areas for future research and current best practices in deriving tsunami fragility functions (section 6). 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.

Development of Tsunami Fragility Functions for Ground-Level Roads

In exploring the relationship between ground-level road damage ratios and tsunami inundation depths following the 2011 Pacific Coast Tohoku earthquake in Japan, we focused on road damage components, excluding elevated roads, bridges, and tunnels. The damage ratio is defined as the number of damage incidents per kilometer. We used the damage dataset compiled by the Japanese Ministry of Land, Infrastructure and Transport. We propose four fragility function zones for ground-level roads based on differences in topographical features. We studied these zones based on numerical simulation results of tsunami propagation.

Understanding the Extreme Tsunami Inundation in Onagawa Town by the 2011 Tohoku Earthquake, Its Effects in Urban Structures and Coastal Facilities

The 2011 Tohoku Tsunami is considered to be one of the most tragic events in Japan's disaster history, and represents an important milestone for the research community regarding the investigation of the characteristics of tsunami inundation. A thorough analysis of tsunami inundation was conducted using numerical modeling, and measurements from a video recorded from the rooftop of a building in Onagawa in Miyagi Prefecture. In this study, we analyze the destruction of buildings using numerical simulations and tsunami fragility functions. Numerical results for the locations at which the tsunami eyewitness video was recorded are compared with measurements. In addition, we considered the effect of the breakwater in Onagawa bay to evaluate its contribution to reducing overland tsunami inundation depths. The results of our simulations show that the maximum inundation depth due to the first incoming wave was over 16 m, and over 500 buildings were washed away with this first wave. This result is consistent with the video data. Further, we found that the breakwater, which was not originally designed against tsunami waves, reduced the maximum tsunami inundation depth at least by 2.0 m in Onagawa town.

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.

Bayesian tsunami fragility modeling considering input data uncertainty

Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.

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

The recent losses caused by the unprecedented 2011 Great East Japan Tsunami disaster have stimulated further research efforts, notably in the mechanisms and probabilistic determination of tsunami-induced damage, in order to provide the necessary information for future risk assessment and mitigation. The stochastic approach typically adopts fragility functions, which express the probability that a building will reach or exceed a predefined damage level usually for one, sometimes several measures of tsunami intensity. However, improvements in the derivation of fragility functions are still needed in order to yield reliable predictions of tsunami damage to buildings. In particular, extensive disaggregated databases, as well as measures of tsunami intensity beyond the commonly used tsunami flow depth should be used to potentially capture variations in the data which have not been explained by previous models. This study proposes to derive fragility functions with additional intensity measures for the city of Kesennuma, which was extensively damaged during the 2011 tsunami and for which a large and disaggregated dataset of building damage is available. In addition to the surveyed tsunami flow depth, the numerically estimated flow velocities as well as a binary indicator of debris impact are included in the model and used simultaneously to estimate building damage probabilities. Following the recently proposed methodology for fragility estimation based on generalized linear models, which overcomes the shortcomings of classic linear regression in fragility analyses, ordinal regression is applied and the reliability of the model estimates is assessed using a proposed penalized accuracy measure, more suitable than the traditional classification error rate for ordinal models. In order to assess the predictive power of the model, penalized accuracy is estimated through a repeated tenfold cross-validation scheme. For the first time, multivariate tsunami fragility functions are derived and represented in the form of fragility surfaces. The results show that the model is able to predict tsunami damage with satisfactory predictive accuracy and that debris impact is a crucial factor in the determination of building collapse probabilities.

Uncertainty modeling and visualization for tsunami hazard and risk mapping: a case study for the 2011 Tohoku earthquake

This study presents a rigorous computational framework for visualizing uncertainty of tsunami hazard and risk assessment. The methodology consists of three modules: (i) earthquake source characterization and stochastic simulation of slip distribution, (ii) tsunami propagation and inundation, and (iii) tsunami damage assessment and loss estimation. It takes into account numerous stochastic tsunami scenarios to evaluate the uncertainty propagation of earthquake source characteristics in probabilistic tsunami risk analysis. An extensive Monte Carlo tsunami inundation simulation is implemented for the 2011 Tohoku tsunami (focusing upon on Rikuzentakata along the Tohoku coast of Japan) using 726 stochastic slip models derived from eleven inverted source models. By integrating the tsunami hazard results with empirical tsunami fragility functions, probabilistic tsunami risk analysis and loss estimation are carried out; outputs from the analyses are displayed using various visualization methods. The developed framework is comprehensive, and can provide valuable insights in promoting proactive tsunami risk management and in improving emergency response capability.

Developing fragility functions for the areas affected by the 2009 Samoa earthquake and tsunami

Abstract. Fragility functions in terms of flow depth, flow velocity and hydrodynamic force are developed to evaluate structural vulnerability in the areas affected by the 2009 Samoa earthquake and tsunami. First, numerical simulations of tsunami propagation and inundation are conducted to reproduce the features of tsunami inundation. To validate the results, flow depths measured in field surveys and waveforms measured by Deep-ocean Assessment and Reporting of Tsunamis (DART) gauges are utilized. Next, building damage is investigated by visually interpreting changes between pre- and post-tsunami high-resolution satellite images. Finally, the data related to tsunami features and building damage are integrated using Geographic Information System (GIS), and tsunami fragility functions are developed based on the statistical analyses. From the developed fragility functions, we quantitatively understood the vulnerability of a coastal region in American Samoa characterized by steep terrains and ria coasts.

Scenarios of Earthquake and Tsunami Damage Probability in Callao Region, Peru Using Tsunami Fragility Functions

The implementation of adequate urban development and measures systems against tsunami impact in coastal communities is improved by understanding damage probability among building structures. Within the framework of the project Enhancement of Earthquake and Tsunami Disaster Mitigation Technology in Peru (JST-JICA SATREPS), the authors analyze the damage probability of building structures due to tsunami impact in the Callao region of Peru. Two different tsunami hazard scenarios are assumed in assessing building damage probability. The first tsunami scenario represents the worse-case scenario of tsunami inundation that calculates the envelop of maximum inundation depth and flow velocity values from 12 probabilistic megathrust earthquake scenarios for central Peru. The second tsunami scenario corresponds to a historical tsunami event in this region. We apply a methodology for evaluating different levels of building damage by combining tsunami numerical results and tsunami fragility functions. Damage probability was analyzed in detail on a single building scale in the La Punta district. For the rest of Callao region, analysis was performed on a block-unit scale. Our results suggest that approximately 30% of submerged building may be washed away by tsunami inundation in the probabilistic hazard scenario and approximately 60% in the historical hazard scenario.