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Characterization of Coastal Flood Damage States for Residential Buildings
Abstract
Risk management programs and catastrophe models use fragility and vulnerability curves extensively. For the case of coastal flood events, the independent variable for these damage functions is usually the inundation depth, sometimes combined with some expression of water velocity or wave action. Postdisaster surveys often provide the basis for these damage functions, where the investigators classify the observed damage into broad categories based on qualitative descriptions. This paper describes a method to transform these qualitative evaluations into quantitative descriptions of damage states, which are then applied to develop fragility and vulnerability curves. The authors present this process within the context of the development of coastal flood fragility and vulnerability functions for the Florida Public Hurricane Loss Model. The model characterizes and quantifies the damage states specific to a set of fragility curves by using damage distributions and component cost analysis and transforms the fragility curves into a vulnerability curve. The paper analyzes the uncertainties in the model due to the number and quantification of the damage states and an adjustment function included in the discretization process. The analysis shows that the number of damage states governs the overall uncertainty. Model outputs are compared with USACE expert opinion depth-damage functions to validate the model and identify aspects for further refinement.
... Based on previous studies (Baradaranshoraka et al. 2019; Paleo-Torres et al. 2020), this paper discusses the methodology developed by the engineering team of the FPFLM to incorporate the effects of mitigation measures, such as the elevation of utilities, wet floodproofing, dry floodproofing, and the elevation of the structure, within residential vulnerability functions. ...
... The subject is a one-story single-family slab-on-grade home of recent (strong) masonry construction. Table 2 [based on the study by Baradaranshoraka et al. (2019)] shows the parameters assigned to the building components' damage PDFs for each of the six damage states (DS). Table 3 shows the cost ratios distributions for the components of the same one-story single-family home in Table 2. ...
... Table 3 shows the cost ratios distributions for the components of the same one-story single-family home in Table 2. The same procedure described by Baradaranshoraka et al. (2019) is used to perform the Monte Carlo simulations (50,000 samples) that produce the mean damage ratios. Sources of uncertainty in the calculations and related statistics can be found in the study by Baradaranshoraka et al. (2019). ...
... The procedure proposed in Barbato et al. [11] is adapted to translate empirical residential tsunami fragility functions from Suppasri et al. [4] into coastal flood fragility functions, based on engineering principles. Following Baradaranshoraka et al. [12], the coastal flood fragility functions translate into coastal flood vulnerability functions for different types of residential structures common in the state of Florida. Insurance claims data and expert-based models are employed for validation. ...
... In the present study, the authors consider the probability of meeting or exceeding a given damage state for residential structures in Japan is similar, but not identical, to the probability of meeting or exceeding the same damage state for a residential structure in Florida under water-induced forces of similar magnitudes. Deviations from this similar damage state assumption are then corrected in the model calibration stage through adjustments to the damage ratios of the different building classes [12]. The key element to translate the tsunami fragility functions into coastal flood fragility functions is the calculation of the different inundation depths that correspond to equivalent water loading forces for tsunami and coastal flood. ...
... Baradaranshoraka et al. [12] proposed a methodology to characterize and quantify the six damage states, following the work from Friedland [29] and Tomiczek et al. [30]. Based on the qualitative description of each damage state, a beta probability density function (PDF) is assigned to each of the components considered in the damage states (foundation, walls, interior, openings and roof). ...
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.
... The columns of the matrix are probability mass functions of damage ratio given an IM. new item of like kind and quality (Baradaranshoraka et al. 2019). In the case of time related expenses, the ratio is the time related expense divided by the maximum policy limit . ...
... Under the sponsorship of FLOIR, the FPHLM team has recently expanded the previously hurricane wind and rain-only scope of the FPHLM to include coastal and inland flood hazards. The team's strategy was to adapt the large body of tsunamirelated building fragility curves, especially the work of Suppasri et al. (2013), to coastal flood, and to adapt the work of the U.S. Army Corp of Engineers (USACE 2006(USACE , 2015 for inland flood through a semi-engineering approach (Baradaranshoraka et al. 2017(Baradaranshoraka et al. , 2019. Regression techniques using the flood claim data are the basis for the development of the flood contents vulnerability curves, as described later in this paper. ...
Catastrophe models estimate risk at the intersection of hazard, exposure, and vulnerability. Each of these areas requires diverse sources of data, which are very often incomplete, inconsistent, or missing altogether. The poor quality of the data is a source of epistemic uncertainty, which affects the vulnerability models as well as the output of the catastrophe models. This article identifies the different sources of epistemic uncertainty in the data, and elaborates on strategies to reduce this uncertainty, in particular through identification, augmentation, and integration of the different types of data. The challenges are illustrated through the Florida Public Hurricane Loss Model (FPHLM), which estimates insured losses on residential buildings caused by hurricane events in Florida. To define the input exposure, and for model development, calibration, and validation purposes, the FPHLM teams accessed three main sources of data: county tax appraiser databases, National Flood Insurance Protection (NFIP) portfolios, and wind insurance portfolios. The data from these different sources were reformatted and processed, and the insurance databases were separately cross-referenced at the county level with tax appraiser databases. The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim. These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios. The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models, and suggests avenues for data improvement. Lessons learned should be of interest to professionals involved in disaster risk assessment and management.
... Although similar nomenclature can be used across different civil engineering subfields to denote performance expectations, their definition and associated damage states vary significantly depending on the type of structure, construction material, and hazard. In the literature, performance objectives and damage classifications have been proposed for various types of coastal structures and hazards (Van De Lindt and Dao, 2009;Van De Lindt and Taggart, 2009;Ciampoli et al., 2011;Li et al., 2012;Massarra, 2012;Ataei and Padgett, 2013;Barbato et al., 2013;Attary et al., 2017;Emanuel, 2017;Park et al., 2017;Tomiczek et al., 2017;Baradaranshoraka et al., 2019;Xiong et al., 2019). PO are commonly associated with structural integrity, operational status, incurred losses, or serviceability requirements (Ciampoli et al., 2011;Barbato et al., 2013;American Society of Civil Engineers, 2017). ...
The changing dynamics of coastal regions and climate pose severe challenges to coastal communities around the world. Effective planning of engineering projects and resilience strategies in coastal regions must not only address current conditions but also take into consideration the expected changes in the exposure and multi-hazard risk in these areas. However, existing performance-based engineering frameworks generally neglect time-varying factors and miss the opportunity to leverage related evidence as it becomes available. This paper proposes a Performance-Based Coastal Engineering (PBCE) framework that is flexible enough to accommodate uncertain time-varying factors, multi-hazard conditions, and cascading-effects. Furthermore, using a dynamic Bayesian network approach, the framework can incorporate observed evidence into the model to update the prior conditional distribution of the analyzed variables. As a proof of concept, two case studies—a typical elevated residential structure and a two-frame system—are presented, considering the effects of cascading failure, the incorporation of time-varying factors, and the influence of emerging evidence. Results show that neglecting cascading effects significantly underestimates the losses and that the incorporation of evidence reduces the uncertainty under the assumed distribution of evidence. The resulting PBCE framework can support data collection efforts, optimization of retrofitting strategies, integration of experts and community interests by facilitating interactions and knowledge sharing, as well as the identification of vulnerable regions and critical components in coastal multi-hazard regions.
... Modeling and simulation lie at the center of the natural hazard community's broader goal to understand, simulate, and predict the performance of built, natural, and social systems during and after natural hazards events . Over the past decade, a portfolio of highly sophisticated natural hazards models has significantly improved our ability to simulate the effects of extreme events across a wide range of spatial and temporal scales (e.g., Roelvink et al., 2009;Dietrich et al., 2011;LeVeque et al., 2011;Pita et al., 2013;Mandli and Dawson, 2014;Yim et al., 2014;Baradaranshoraka et al., 2019). These natural hazards models have become increasingly data-driven, requiring comprehensive data sets to capture complex, system-level responses. ...
Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines. © Copyright © 2020 Wartman, Berman, Bostrom, Miles, Olsen, Gurley, Irish, Lowes, Tanner, Dafni, Grilliot, Lyda and Peltier.
... Flood vulnerability analysis of the built environment is gaining attention in recent decades and many researchers are paying attention to develop flood vulnerability functions (see e.g. (Fuchs et al., 2019a,b;Baradaranshoraka et al., 2019;De Risi et al., 2019), among others). The vulnerability, as well as fragility, functions depict the probability of occurrence of particular damage under the flood intensity level. ...
Study region: This study considers the Khando River (a tributary of Koshi River) in eastern Nepal.
Study focus: To quantify the hazard and vulnerabilities across one of the frequently flooding catchments, i.e. Khando River, we conducted flood hazard assessment for 20, 50, 100, and 200 years return periods. We coupled flood hazard analysis with vulnerability analysis of the most dominant construction system along the river channel, i.e. wattle and daub houses. Based on the measured inundation depths, we created vulnerability and fragility functions. The flood hazard maps, damage mechanisms due to the 2017 flood, and vulnerability, as well as fragility, curves are reported in this paper.
New Hydrological Insights for the Region: The flood hazard analysis highlighted that the 2017 flood was equivalent to 20 years return period flood. Flood hazard analysis shows that the variation in the maximum inundation depth is not so wide between 20 and 200 years return periods for the Khando River catchment. Flood vulnerability analysis of residential houses along the riverbank highlighted that the wattle and daub construction system is highly vulnerable even for 20 years return period flood. Thus, the floods equivalent to 50, 100, and 200 years may have detrimental consequences in the future.
... As mentioned previously, the current literature is still lacking a complete fragility portfolio for an array of building archetypes, and research is needed to support the development of those fragilities. However, there are several studies that have made some advances in introducing numerical flood fragility curves (Baradaranshoraka et al. 2019;De Risi et al. 2013) and empirical fragilities (van de Lindt et al. 2018). In this study, the empirical fragilities that were developed by the Lumberton field study participants for the residential buildings of Lumberton will be used, i.e., noting that these are generalized across all residential building types and characterized only by foundation type which is crawlspace versus slabon-grade. ...
Flood events are one of the most common natural disasters in the United States and can disrupt businesses; strain the financial resources of agencies that respond; and often leave households dislocated for days, months, or permanently. Community resilience planning requires an assessment of the damage and loss caused by a hazard followed by recovery modeling, which couples the socioeconomics with the physical-infrastructure recovery process. This paper focuses on the first part of that analysis chain, namely damage and loss modeling to riverine flooding at the community level, with a case study of Lumberton, North Carolina, using empirical damage fragilities. The process includes the major components toward flood-loss quantification. The losses in the case study are computed from the damage fragilities and compared with the deterministic flood loss analysis in HAZUS-MH, which uses stage-damage functions. For the case study presented in this paper, the fragility-based approach resulted in slightly higher loss estimates. The fragility-based approach presented as part of this study can provide a mechanism to propagate uncertainty in damage and loss estimates. This ability to propagate such uncertainty into the analysis would allow for risk-informed decision making for floods using a similar approach to what is currently done for earthquake and wind community-level loss analyses.
... Here, we provided an overview of flash flood damages and we encourage further investigation of the linkage between flood damages. Several damage prediction analyses have been conducted for various natural hazards such as hurricanes (Burton, 2010;Kim et al., 2016;Van Verseveld et al., 2015) and coastal flooding (Baradaranshoraka et al., 2019;de Moel et al., 2012;Hinkel et al., 2014). A rigorous modeling of flash flood damages can be helpful for implementing proactive risk management strategies. ...
Flash floods are among the most disastrous natural hazards worldwide because of their rapid onset that limits effective emergency response and management. Understanding the spatiotemporal characteristics of flash floods can help emergency managers and stakeholders in devising solutions to mitigate the associated risks and damages. In this study, a data-driven approach is employed to characterize flash flood hazard across the contiguous United States (CONUS). The National Weather Service (NWS) Storm Events database is utilized, and a total of 74,814 flash flood events are assessed during 1996–2017. The frequency, duration, property damages, and fatalities of flash floods are investigated across spatial domains (i.e. county, state, regional, and entire CONUS) and multiple temporal scales (i.e. annual, seasonal, monthly, and diurnal). Results indicate that flash flood frequency has slightly increased over the CONUS in the past 22 years, and the property damages have substantially raised. Flash floods are found to be more frequent in summer and less frequent but longer in winter. Our analyses indicate distinct regional patterns for duration of median versus extreme flash floods. In addition, the diurnal distribution of flash floods indicate that they are more likely to initiate in the evening and terminate around midnight. The average duration of flash floods in the past 22 years has been about 3.5 hours, yet in some rare cases, they persisted for about two days. The findings of this study can shed light on flash flood hazard across the CONUS, which can be useful for mitigating flash flood risks.
... Then, the model calculates, using Monte Carlo simulations, the ALE ratio as a ratio between the sum of the delay time and repair time, calculated in the previous steps, and the maximum delay and maximum repair time, defined in the FPHLM model. After calculating the resulting pdf's of ALE ratio, within each wind speed intervals, the resulting ALE vulnerability curves are the mean values of each pdf as a function of the wind speed [5]. ...
Hurricanes are a main cause of economic losses in many parts of the world, including the state of Florida. They affect the exterior, interior, and the contents of residential buildings. The damage of the interior can be as much as half of the total damage, but the estimation of the interior damage is still somewhat crude. Most damage models estimate interior damage as a percentage of the exterior damage, or through simple relationships, that relate the height of water ingress in the building to the interior damage. This paper presents a new methodology for interior damage estimation, which takes into account the physical mechanisms of hurricane-induced interior damage. The method incorporates the results of large-scale tests to quantify the water propagation and distribution from component to component in the interior of the building. The paper describes the methodology and presents the resulting building, contents, and time-related expenses vulnerability curves.
The Philippines experiences frequent flooding, but, despite expansive tools for risk reduction, there remain gaps in understanding generalised relationships between flood events and damage to residential structures for regions outside the nation's capital. This gap has limited the ability to model flood risk and damage without robust functions to link hazards and housing vulnerability. This research draws on 394 household surveys to empirically derive a suite of flood fragility and vulnerability functions for residential structures in the Province of Leyte for light material, elevated light material and masonry structures. The results showed that masonry construction was more resilient to floods compared to light material counterparts. Elevated light material structures also exhibited lower damages at low inundations but tend to fail abruptly at flood depths greater than 3 m. By empirically deriving flood damage functions, the findings contribute to a more localised approach to quantifying housing vulnerability and risk that can be used for catastrophe and risk modelling, with applications for government agencies, the insurance industry and disaster risk researchers. This research lays the foundation for future flood risk mapping with growing significance under climate change.
Scientific community showed particular interest in tsunami risk assessment in the last years due to several worldwide tsunami events where high damages and human casualties were recorded during post-event surveys.
This paper investigates the behaviour of Italian coastal residential masonry buildings under tsunami loads. Main fragility curves available in literature are based on empirical databases and consequentially, the structural behaviour is strictly related to local building typologies of other countries.
For masonry buildings, it is complex to predict global response due to high material inhomogeneity, especially in the case of regional scale analysis; a critical review on local mechanisms activation of masonry walls under tsunami loads is briefly performed. Therefore, local mechanisms activation for masonry walls are investigated depending on out-of-plane and in-plane mechanisms assuming flexural and shear failure.
Structures designed for gravity or seismic loads are generated based on several building classes depending on number of storeys and age of construction. Monte Carlo analyses generated building models for each identified building class to simulate the behaviour of Italian masonry buildings.
Preliminary analytical tsunami fragility curves are developed in order to assess the vulnerability of Italian coastal residential masonry buildings under tsunami loads. A critical review is proposed on developed fragility curves.
Hurricane Irma made its first landfall in the continental US at Cudjoe Key in southern Florida on September 10, 2017, with Category 4 winds. Irma made its second landfall later that afternoon
on Marco Island on Florida’s Gulf Coast, as a Category 3 storm, weakening further as it moved north. Reconnaissance teams respectively dedicated to the Gulf Coast, Atlantic Coast and Miami/Florida Keys carried on a first wave of rapid reconnaissance from September 15-20, 2017. A second more exhaustive campaign was then conducted in the Florida Keys on September 22-25, 2017. These investigations employed primarily door-to-door damage assessments to classify buildings and evaluate component damage levels using a Fulcrum mobile smartphone application for data collection in a unified assessment framework feeding into a quality assurance/quality control process. At select locations, unmanned aerial surveys generated aerial imagery, 3D point clouds and 3D textured meshes. This study presents the geospatial distribution of observed damage, correlated by age of construction, typology, construction materials, and estimated wind speed. Specific case studies highlight common failure modes observed in different construction classes and editions of the Florida Building Code.
With 6.5 million households in the United States located in areas prone to storm surge, assessing the vulnerability of structures and residential communities to coastal flooding is an important concern. Of particular interest is understanding how the performance of structures during surge events is influenced by flood conditions, coastal defenses, and building design at both the house and community level. This study presents such a vulnerability analysis by investigating the impact of coastal flooding from Hurricane Sandy (2012) on 95 km of developed coastline across New Jersey and New York. Exterior structural damage observed from a Federal Emergency Management Agency (FEMA) aerial survey of the study area is related to (1) a hindcast simulation of flood inundation and wave action, (2) a classification of coastal defenses and storm-induced erosion, and (3) minimum building design elevations specified in FEMA’s flood hazard mapping. Findings indicate that the hardest hit communities experienced significant dune erosion, which coincided with severe flood and wave exposure. Furthermore, structures were considerably more susceptible to flood impact if Sandy’s flood level exceeded FEMA’s 100-year Base Flood Elevation dictating building design. These results are quantified by developing fragility curves relating both house and community-level performance to the considered vulnerability parameters. Findings from this study can be used to inform decision making for improving coastal resilience.
Regional-scale and local damage surveys of the U.S. Mid-Atlantic coast were performed after Hurricane Sandy in 2012. A satellite-based analysis of over 15,000 houses within one block of the New Jersey, Long Island, and Staten Island coastlines showed a strong correlation between destruction and poststorm dune heights. A detailed survey in Ocean County, New Jersey, classified 380 homes into seven damage states to different subassemblies. A phase-resolving Boussinesq-Green-Naghdi wave model simulating the strongest hour of the storm was used to evaluate hydrodynamics at each residence. Maximum computed water surface elevations were found to differ strongly from standard depth-limited assumptions. A vulnerability model to diagnose the damage state of a coastal residence subject to storm conditions identified maximum water velocity and relative shielding as critical predictors of damage. Improved hydrodynamic models that can efficiently compute the complex flow interactions with structures may provide more reliable damage prediction in coastal communities.
Increasing interest in fostering resilient communities requires a more comprehensive approach to hazard mitigation planning that overcomes the limitations of traditional hazard assessments, notably the failure to explicitly incorporate an analysis of social vulnerability. We statistically analyzed a random sample of 1500 damage assessments of single-family homes collected following Hurricane Ike to assess the contributions of hazard exposure, structural vulnerability, and social vulnerability. The results indicate that hazard exposure, structural characteristics, and socioeconomic characteristics are significant predictors of structural damage. The implications suggest that comprehensive hazard assessments can provide additional insights for mitigation planning and community resiliency.
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 account in the damage probability analysis. Using least squares regression, different sets of fragility curves are derived to demonstrate the influence of structural material, number of stories and coastal topography on building damage levels. The results show a better resistant performance of reinforced concrete and steel buildings over wood or masonry buildings. Also, buildings taller than two stories were confirmed to be much stronger than the buildings of one or two stories. The damage characteristic due to the coastal topography based on limited number of data in town locations is also shortly discussed here. At the same tsunami inundation depth, buildings along the Sanriku ria coast were much greater damaged than buildings from the plain coast in Sendai. The difference in damage states can be explained by the faster flow velocities in the ria coast at the same inundation depth. These findings are key to support better future building damage assessments, land use management and disaster planning.
This paper presents recent advances in knowledge on wave loads, based on experimental work carried out in the CIEM/LIM large flume at Barcelona within the framework of the VOWS (Violent Overtopping by Waves at Seawalls) project. Both quasi-static and impact wave forces from the new data set have been compared with predictions by empirical and analytical methods. The scatter in impact forces has been found to be large over the whole range of measurements, with no existing method giving especially good predictions. Based on general considerations, a simple and intuitive set of prediction formulae has been introduced for quasi-static and impact forces, and overturning moments, giving good agreement with the new measurements. New prediction formulae have been compared with previous measurements from physical model tests at small and large scale, giving satisfactory results over a relatively wide range of test conditions. The time variation of wave impacts is discussed, together with pressure distribution up the wall, which shows that within experimental limitations the measured pressures are within existing limits of previous study.
On December 26, 2004 a great earthquake (M
W 9.3) occurred off the western coast of Sumatra triggering a series of tsunami waves that propagated across the Indian Ocean causing damage and life loss in 12 countries. This paper summarizes the observations of lifeline performance, building damage and its distribution, and the social and economic impact of the tsunami made by the Earthquake Engineering Field Investigation Team (EEFIT) in Thailand and Sri Lanka. EEFIT operates under the umbrella of the UK’s Institution of Structural Engineers. It is observed that good engineering practice can reduce economic losses, but additional measures are required to reduce risk to life.
This paper investigates the potential effect of climate change on future hurricanes and the corresponding losses in residential buildings. Four climate change scenarios projected by the United Nations Intergovernmental Panel on Climate Change in 2013 are adopted. Specifically, the effect of sea surface temperature on hurricanes generated in the North Atlantic Ocean and making landfall in Florida is studied, which is then used to observe the impact of the change in sea surface temperature caused by climate change on future hurricane damage and loss for Miami-Dade County. It is found that when the North Atlantic sea surface temperature increases by a degree Celsius, the 3-s gust wind speed with a recurrence interval of 700 years increases by roughly 6.7-8.9 m/s for the county. This results in the accumulated hurricane loss for the county from 2016 to 2055 considering the climate change scenarios to be 1.4-1.7 times the value calculated based on 2006 climate conditions.
Annualized hurricane-related losses in the United States are in the billions of dollars. The majority of the coastal population lives in buildings prone to hurricanes, which could result in wind damage (due to high wind and/or wind-driven rain infiltration) and water damage (due to storm surge and waves or freshwater flooding), which are typically insured by separate entities. In the event that a structure is subjected to damage from both wind and water hazards, the proper allocation of damage for insurance purposes is challenging because wind and water vulnerability are often assessed with separate models. The challenge is how to combine the results of these different models and properly assign damage causation. This paper proposes a methodology for the assessment of multihazard vulnerability for implementation in the Florida Public Hurricane Loss Model, in which the damage from each hazard (wind and rain, storm surge, and freshwater inundation) is calculated separately. An engineering approach in conjunction with hazard information (intensity, timing) and statistical methods is utilized to allocate damage.
Florida's population growth in recent decades has produced a steady increase in the concentration of population and infrastructure along its coastline. During this growth period, the vulnerability of the Florida coastline to hurricane impact has been amply demonstrated. The aftermaths unveiled the underlying potential for dramatic economic loss, which is disruptive for the state economy and has created a crisis in the property insurance market. To aid in preparedness efforts, the state of Florida commissioned a group of experts to develop the Florida Public Hurricane Loss Model (FPHLM). This paper describes how hurricane damage prediction techniques were developed and implemented in the vulnerability module of the FPHLM. The module consists of an external damage simulation engine coupled with internal damage and contents damage simulation engines. These components are described in this paper.
The present paper introduces a vulnerability modelling approach for residential buildings in flood. The modelling approach explicitly considers relevant damage processes, i.e. water infiltration into the building, mechanical failure of components in the building envelope and damage from water contact. Damage processes are modelled at a building component level, utilising engineering models where possible. The modelling approach is presented in general terms, which should be applicable to a large variety of building types. The paper illustrates the implementation of the approach for a 2-storey masonry building. Results are presented in terms of a parameter study for several building parameters and hazard characteristics, as well as, in terms of a comparison with damage data and literature vulnerability models. The parameter study indicates that hazard characteristics and building characteristics impact damage ratios as expected. Furthermore, the results are comparable to vulnerability models in literature. Strengths and shortcomings of the model are discussed. The modelling approach is considered as a step towards the establishment of vulnerability models that can serve as a basis for engineering decision-making for flood risk management for residential buildings.
This paper presents a comprehensive review of methods to assess building vulnerability for hurricane catastrophe models. The review identified five main types of assessment approaches judging by the underlying methodology: past-loss data, enhanced damage data, heuristic, physics, and simulation. The applicability of past-loss data-only vulnerability methods proved insufficient for the diversity of situations insurance companies faced. Therefore, modelers complemented this method with engineering and meteorology expert knowledge; these are the enhanced-data models. Expert opinion and subjective probabilities drive the heuristic models; these were short lived in the United States, but are still used when data are scarce. Component-based methods were developed as a more realistic alternative to enhanced-data models by assessing vulnerability within an engineering framework complemented with expert opinion. Simulation models enhanced the physical models with a probabilistic simulation of the wind-structure interaction and more realistic assessment of the hazard. This paper also reviews some influential postdisaster studies utilized for validation. In addition, this paper presents a temporal and spatial map showing linkages between models. The development of interior damage models, as well as the future of vulnerability models and possible applications, is discussed.
There is a wealth of existing fragility curves for buildings and infrastructure. The main challenge in using these curves for future applications is how to identify and, if necessary, combine suitable fragility curves from a pool of curves with different characteristics and, often unknown, reliability. The present chapter aims to address this challenge by developing a procedure which identifies suitable fragility curves by firstly assessing their representativeness to the needs of the future application and then assessing the reliability of the most relevant relationships. The latter is based on a novel procedure which involves the assessment of the most significant factors affecting the robustness and quality for each fragility assessment methodology, also presented here. In addition, a decision-tree approach is adopted in order to combine more than one suitable fragility curves. The proposed selection and combination procedures are illustrated here with a simple case study which appraises the impact of different weighting schemes and highlights the importance of a deep understanding of the existing fragility curves and their limitations.
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As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The state of Florida has developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics, thousands of storms are simulated allowing determination of the wind risk for all residential Zip Code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss, respectively. The actuarial team finds the county-wise loss and the total loss for the entire state of Florida. The computer team then compiles all information from atmospheric science, engineering and actuarial components, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised as per the suggestions of the commission. The final model was approved for use by the insurance companies in Florida by the commission. At every stage of the process, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology, vulnerability and actuarial) and then focuses on the statistical validation of the same.
The authors explain the ways in which uncertainty is an important factor in the problems of risk and policy analysis. This book outlines the source and nature of uncertainty, discusses techniques for obtaining and using expert judgment, and reviews a variety of simple and advanced methods for analyzing uncertainty.
The prevailing Italian and Greek methodologies for seismic risk assessment are used herein to construct loss scenarios for the building stock of a small city (Potenza, Southern Italy). The inventory of buildings of interest is obtained from a survey carried out after the 1990 earthquake that struck Potenza and its hinterland, subsequently updated in 1999. About 12,000 buildings were surveyed in Potenza, using the Italian first level survey form for damage and vulnerability evaluation. In the Italian methodology, a hybrid technique is set up to evaluate vulnerability, combining an analysis of building typologies with expert judgement. The probabilistic distribution of damage is evaluated by assigning Damage Probability Matrices (DPMs) from the literature. Besides the vulnerability classes A, B and C of the MSK-scale, the class D of the anti-seismic buildings is considered and the relevant DPM is defined. Damage and economic loss scenarios relevant to dwelling buildings are constructed for three reference earthquakes. Next, the hybrid methodology for seismic vulnerability assessment of reinforced concrete (R/C) and masonry buildings developed at the University of Thessaloniki (Greece) is applied to the same building stock. The methodology combines available statistical data of damage collected after past earthquakes with a systematic nonlinear analysis of various “model buildings”, representative of several vulnerability classes. Similarities, as well as discrepancies, between the two methods are discussed in the light of the obtained results, and possible sources for the discrepancies are suggested.
The sources and characters of uncertainties in engineering modeling for risk and reliability analyses are discussed. While many sources of uncertainty may exist, they are generally categorized as either aleatory or epistemic. Uncertainties are characterized as epistemic, if the modeler sees a possibility to reduce them by gathering more data or by refining models. Uncertainties are categorized as aleatory if the modeler does not foresee the possibility of reducing them. From a pragmatic standpoint, it is useful to thus categorize the uncertainties within a model, since it then becomes clear as to which uncertainties have the potential of being reduced. More importantly, epistemic uncertainties may introduce dependence among random events, which may not be properly noted if the character of uncertainties is not correctly modeled. Influences of the two types of uncertainties in reliability assessment, codified design, performance-based engineering and risk-based decision-making are discussed. Two simple examples demonstrate the influence of statistical dependence arising from epistemic uncertainties on systems and time-variant reliability problems.
Vulnerability model of residential structures subject to hurricane induced wind surge and inundation
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Maintenance and improvement of the Florida public hurricane loss model
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US north Atlantic coast comprehensive study: Resilient adaptation to increasing risk. Physical damage function summary report
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