Hazard Intensity Measures Research Articles

Evaluation of debris-flow building damage forecasts

Abstract. Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

Nonresilience curve: A new tool for use in resilience assessment

AbstractResilience assessment is a widely used method to evaluate the ability of an object (e.g., an individual structure, or a system consisting of multiple interacting structures) to withstand, recover from, and adapt to disruptive events. This paper proposes a novel concept of “nonresilience curve,” which measures the nonresilience (complement of resilience) of an object of interest conditional on a specific hazard intensity. It is by nature an extension of the well‐established fragility curves, integrating the multiple damage states of a posthazard object. The applicability of the proposed nonresilience curve to individual structures and systems (including series systems, parallel systems, and more general and complicated systems) has been demonstrated in this paper. It is also preliminarily shown that the shape of the cumulative distribution function of a lognormal distribution is suitable to approximate the nonresilience curve, if only limited data points associated with the target nonresilience curve are available. Since the nonresilience curve is a function of the hazard intensity measure (IM), one can estimate the nonresilience of an object in a fully probabilistic manner by additionally taking into account the uncertainty associated with the IM. The proposed nonresilience curve can be further extended to formulate nonresilience surface, which is a joint function of both the IM and the available resource that supports the posthazard recovery process. The nonresilience curve is promising to be adopted in engineering practice for resilience assessment and resilience‐based design of civil structures and infrastructure systems.

Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions

The offshore wind industry is rapidly developing in a hurricane prone area off the US East Coast. For assessment of extreme environmental conditions, or hazard intensity measures, such as wind and wave loads (e.g., 50-year or 500-year wind speed) various methodologies including univariate (e.g., Generalized Extreme Value distribution; GEV) and multivariate (e.g., Inverse First Order Reliability Method, IFORM) analysis have been recommended. Further, due to lack of long-term observed data at a site, available observed/hindcast at nearby stations are commonly used which can lead to errors. The objective of this study is to better understand and quantify the level of uncertainty in extreme value analysis of wind/wave data for marine renewable energy sites in hurricane-prone regions. An area off the northeast of the US where several large projects have been planned was selected as a case study. Univariate and bivariate analyses of wind/wave data at several stations were carried out and results were compared.For this study, Bivariate (IFORM) and univariate (GPD) methods resulted in an average difference of 4% and 6% for extreme wind and wave data, respectively. It has been demonstrated that these discrepancies still fall within the 95% confidence intervals of the standard GEV method.

Multi-indicator building vulnerability index for assessing tsunami-induced building damages

While relatively uncommon, tsunami typically leads to extreme and devastating impacts to human lives and development at far distances. Various international bodies and academic research have highlighted the importance of understanding risk and its determinants for effective risk management. Currently available methodologies to assess tsunami risk and the effect of vulnerability towards the resulting impact present opportunities for improvement particularly in their adaptability, accuracy and generalisability for pre- and post-disaster applications, as well as across temporal and geographical boundaries. This paper discusses the potential of a methodology to assess tsunami risk for buildings based on their vulnerability level, which in turn is determined through a set of vulnerability indicators. A vulnerability index equation consisting of four indicators namely building materials, number of storeys, coastal protective structures and coastal vegetation was formulated through the Analytical Hierarchy Process. The index is then applied for two major yet distinctly different tsunami events for the purpose of examining potential links between hazard intensity measure, building vulnerability and recorded impacts while also establishing basis for generalisability. While certain correlations can be observed, the findings are still at preliminary stage and thus approaches to further refine the index were proposed.

Agriculture and forestry impact assessment for tephra fall hazard

Developing approaches to assess the impact of tephra fall to agricultural and forestry systems is essential for informing effective disaster risk management strategies. Fragility functions are commonly used as the vulnerability model within a loss assessment framework and represent the relationship between a given hazard intensity measure (e.g., tephra thickness) and the probability of impacts occurring. Impacts are represented here using an impact state (IS), which categorises qualitative and quantitative statements into a numeric scale. This study presents IS schemes for pastoral, horticultural, and forestry systems, and a suite of fragility functions estimating the probability of each IS occurring for 13 sub-sectors. Temporal vulnerability is accounted for by a ‘seasonality coefficient,’ and a ‘chemical toxicity coefficient’ is included to incorporate the increased vulnerability of pastoral farming systems when tephra is high in fluoride. The fragility functions are then used to demonstrate a deterministic impact assessment with current New Zealand exposure.

Order Matters: The Benefits of Ordinal Fragility Curves for Damage and Loss Estimation.

Probabilistic loss assessments from natural hazards require the quantification of structural vulnerability. Building damage data can be used to estimate fragility curves to obtain realistic descriptions of the relationship between a hazard intensity measure and the probability of exceeding certain damage grades. Fragility curves based on the lognormal cumulative distribution function are popular because of their empirical performance as well as theoretical properties. When we are interested in estimating exceedance probabilities for multiple damage grades, these are usually derived per damage grade via separate probit regressions. However, they can also be obtained simultaneously through an ordinal model which treats the damage grades as ordered and related instead of nominal and distinct. When we use nominal models, a collapse fragility curve is constructed by treating data of “near‐collapse” and “no damage” the same: as data of noncollapse. This leads to a loss of information. Using synthetic data as well as real‐life data from the 2015 Nepal earthquake, we provide one of the first formal demonstrations of multiple advantages of the ordinal model over the nominal approach. We show that modeling the ordering of damage grades explicitly through an ordinal model leads to higher sensitivity to the data, parsimony and a lower risk of overfitting, noncrossing fragility curves, and lower associated uncertainty.

A model taxonomy for flood fragility and vulnerability assessment of buildings

In the last two decades, probabilistic approaches to flood risk modeling have emerged, often as an extension of more consolidated methods used in probabilistic seismic risk assessment. Nonetheless, only a few studies deal with best-practice methodologies for flood physical vulnerability assessment, and existing approaches/models often lack appropriate guidance for their selection/rating and use. These concerns underline the need for a rational, integrated and comprehensive compendium of existing flood-related fragility (i.e., the likelihood of various damage states as a function of hazard intensity measure(s)) and vulnerability (i.e., the likelihood of loss levels as a function of hazard intensity measure(s)) models to be used in probabilistic flood risk assessment. To this aim, and following the approach used in the guidelines recently developed by the Global Earthquake Model (GEM) project, this paper proposes a model taxonomy for flood fragility and vulnerability assessment of buildings. A review of state-of-the-art large-scale models for flood vulnerability assessment is first carried out. A discussion on the main factors affecting the reliability of empirical fragility and vulnerability relationships is presented, focusing on data sources, building classification, statistical techniques for data collection/fitting, and damage scales/loss metrics. As a proof of concept, a compendium of existing studies dealing with empirical fragility and vulnerability models for buildings is finally developed and discussed based on the proposed model taxonomy. This type of database can benefit (re)insurance companies interested in flood loss assessment and various decision-makers (e.g., governmental agencies) committed to mitigate flood risk and communicate its level to different stakeholders.

Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

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.

An efficient algorithm to simulate hazard-targeted site-based synthetic ground motions

This study presents an efficient algorithm that can be used to simulate ground motion waveforms using the site-based approach developed by Dabaghi and Der Kiureghian, and Rezaeian and Der Kiureghian that not only correspond to a specified seismic scenario (e.g. magnitude, distance, site conditions) but are also certain to achieve a target ground motion intensity measure within a narrow range. The suggested algorithm alleviates the need to scale simulated ground motions generated using the above-mentioned site-based approach; the resulting hazard-targeted simulated ground motions have consistent amplitude and time- and frequency-domain characteristics, which are required for proper seismic demand analysis of structures. The proposed algorithm takes as input a set of seismic Event Parameters and the target hazard intensity measure [Formula: see text] and generates a corresponding set of Model Parameters (i.e. input to the site-based ground motion simulation model). These Model Parameters are then used to simulate ground motion waveforms that not only represent the set of input Event Parameters ( Mw, Rrup, Vs30) but also maintain the target [Formula: see text]. To generate the set of Model Parameters, predictive relations between the Model Parameters and [Formula: see text] of ground motions are developed. Among the Model Parameters, the ones classified as important by statistical procedures (such as Random Forests, Forward Selection) are used to develop the predictive relations. The developed relations are then validated against a large dataset of recorded ground motions. The final implementation is provided in terms of graphic-user interface (GUI) called “Hazard-Targeted Time-Series Simulator” ( HATSim), which efficiently simulates site-based ground motions with minimum inputs.

A reliability-based approach for the impact vulnerability assessment of bridge piers subjected to debris flows

Bridge piers are typically among the most vulnerable structural members subjected to debris flows. This paper proposes a reliability-based approach based on a nonlinear structural analysis to determine the physical vulnerability curves associated with bridge piers given exposure to debris flow impact. Relevant aleatory and epistemic uncertainties in both hazard and structure are incorporated in the method with the inclusion of a fully probabilistic framework. The high- and low-viscosity debris flows are assessed as being representative of two classes of debris flow for their different load patterns. The load effect on a pier column is considered as the combined effect of slurry and boulder impact, which can be characterized, respectively, by Poisson square wave process and Poisson impulse process. A quantitative displacement-based damage index is defined considering the possible failure modes of the pier column. Using a Monte Carlo simulation, a set of vulnerability curves relative to different hazard intensity measures for high- and low-viscosity debris flow is derived and sensitivity studies are conducted to estimate the influences of hazard inputs. The proposed methodology addresses the geographical restrictions of conventional empirical debris flow vulnerability models and provides a general approach for quantitative risk assessment in hazard-prone areas, thereby forming a basis for subsequent engineering design or risk zoning.

Tsunami impact assessment: development of vulnerability matrix for critical infrastructure and application to Christchurch, New Zealand

Tsunamis can have severe impacts on society. In addition to casualties and damage to buildings, they can also damage and disrupt critical infrastructure. To support effective risk management, it is important to understand the possible extent, severity and duration of these impacts. While impacts on buildings and casualty estimations are relatively well developed for tsunamis, critical infrastructure impact models are either not available or lack accurate vulnerability information. This paper addresses these issues by reviewing global tsunami impacts to critical infrastructure to develop a semi-quantitative tsunami damage matrix for critical infrastructure components and systems. One potential application of the damage matrix is demonstrated within an impact assessment process for Christchurch, New Zealand. The impact assessment determines asset damage likelihood for energy, water, telecommunication and transportation assets from a large tsunami inundation scenario. Consistent with observations from previous international tsunamis, above-ground assets and buried storm water pipe networks in Christchurch were found to perform poorly, while buried potable and waste water pipe networks were less impacted. This process provides a simple, but widely applicable, impact assessment approach which further studies can build on. Future research should focus on developing quantitative probabilistic tsunami vulnerability models for critical infrastructure that implement multiple asset standards and hazard intensity measures, and better account for uncertainty.

Physical vulnerability models for assessing building damage by debris flows

A catastrophic debris flow occurred in Zhouqu County, Gansu Province, Northwestern China, at midnight on 7 August 2010, resulting in damage to >390 buildings. Assessment of the vulnerability of buildings to debris flow hazards is essential to quantify the risk of debris flows. This paper presents a new family of physical vulnerability curves to assess the damage to buildings by debris flows based on extensive observed data of building damage and simulated debris flow intensity. A database of damaged buildings in the affected area of the Zhouqu debris flow was reconstructed. Numerical simulations using FLO-2D were conducted to determine the intensity of debris flows. A series of physical vulnerability empirical models for assessing the building damage has been proposed using various hazard intensity measures. Since buildings of different structural types have different resistances to debris flows, the vulnerability functions were developed for two typical types of structures, i.e. brick-concrete structures and reinforced concrete-frame structures. The proposed vulnerability functions are comparable with those for debris flows in Switzerland, Italy and Austria. The buildings may be completely destroyed when the debris flow depth is more than one half of the building height for both types of building structures.

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

AbstractPyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three‐stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D‐PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano‐specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma‐Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma‐Vesuvius and worldwide.

Epistemic Uncertainties in Fragility Functions Derived from Post-Disaster Damage Assessments

AbstractFragility functions define the probability of meeting or exceeding some damage measure (DM) for a given level of engineering demand (e.g., base shear) or hazard intensity measure (IM; e.g.,...

Hurricane risk assessment of offshore wind turbines

Abstract A barrier to the development of the offshore wind resource along the U.S. Atlantic coast is a lack of quantitative measures of the risk to offshore wind turbines (OWTs) from hurricanes. The research presented in this paper quantifies the risk of failure of OWTs to hurricane-induced wind and waves by developing and implementing a risk assessment framework that is adapted from a well-established framework in performance-based earthquake engineering. Both frameworks involve the convolution of hazard intensity measures (IMs) with engineering demand parameters (EDPs) and damage measures (DMs) to estimate probabilities of damage or failure. The adapted framework in this study is implemented and applied to a hypothetical scenario wherein portions of nine existing Wind Farm Areas (WFAs), spanning the U.S. Atlantic coast, are populated with ∼7000 5 MW OWTs supported by monopiles. The IMs of wind and wave are calculated with a catalog representing 100,000 years of simulated hurricane activity for the Atlantic basin, the EDPs are calculated with 24 1-h time history simulations, and a fragility function for DM is estimated by combining variability observed in over one hundred flexural tests of hollow circular tubes found in the literature. The results of the study are that, for hurricane-induced wind and wave, the mean lifetime (i.e., 20-year) probability of structural failure of the tower or monopile of OWTs installed within the nine WFAs along the U.S. Atlantic coast ranges between 7.3 × 10−10 and 3.4 × 10−4 for a functional yaw control system and between 1.5 × 10−7 and 1.6 × 10−3 for a non-functional yaw control system.

Robust bridge design to blast, fire, and other extreme threats

Recent bridge projects have incorporated multiple hazards (i.e., blast, fire, manual sabotage, rocket-propelled grenade/mortar) as independent threats on the system. Traditional design methods to handle each threat separately are expensive and can lead to conflicting requirements. This paper will i ntroduce the framework for a robustness-based design process. The outcomes are articulated through a series of generalized variables; topology (i.e., structural configuration relative to the site or location), geometry (i.e., layout of the structural load bearing elements), damage, and hazard intensity measures. A probabilistic framework permits consistent characterization of the inherent uncertainties through the process. Rather than consider the global resistance as a sliding scale in relation to a fixed load, the proposed alternative is to consider robustness as a fixed property of the system, that is, Robustness is a function of topology and geometry. Geometry and topology are absolute properties that cannot be changed without modifications to the overall system configuration. If robustness is held to be an absolute property of the system, then resilience represents the variable property that fluctuates with specific design decisions. If element failure (i.e., collapse) is avoided, damaged elements will still require repair or replacement, resulting in a temporary loss of functionality. Resistance should, therefore, be provided such that potential damage minimizes casualties and reduces the likelihood of catastrophic structural losses. The proposed expression of resilience is a function of hazard and robustness, or a function of hazard, topology and, geometry. The structural performance associated with a specific system configuration is considered as independent from the contribution of component strengthening to address a prescribed load or hazard. The resulting equation for resilience represents the specific hazard magnitude mitigated by a structural design with an assigned robustness. This definition of resilience allows engineers to quantify resilience and robustness in more certain terms and provides a basis to better assess post-event structural behavior.

Effects of Residential Exposure to Physical Earthquake Damage Caused by the 2010/2011 Canterbury Earthquakes on Mood and Anxiety Disorders in Christchurch

Effects of Residential Exposure to Physical Earthquake Damage Caused by the 2010/2011 Canterbury Earthquakes on Mood and Anxiety Disorders in ChristchurchAbstract Number:1761 Daniel Hogg*, Simon Kingham, Thomas Wilson, Edward Griffin, Michael Ardagh Daniel Hogg* GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Simon Kingham GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Thomas Wilson Department of Geological Sciences, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , Edward Griffin GeoHealth Laboratory, Department of Geography, University of Canterbury, New Zealand, E-mail Address: [email protected] Search for more papers by this author , and Michael Ardagh Canterbury District Health Board (CDHB), New Zealand, E-mail Address: [email protected] Search for more papers by this author AbstractObjective: In the 2010/2011 Canterbury earthquake sequence 185 people were killed, over 8,000 injured, over 100,000 buildings have been damaged and on-going aftershocks kept the city on edge maintaining high anxiety levels. This project examined whether mood and anxiety disorders up to sixteen months after the devastating 22nd February 2011 earthquake were more likely to develop in people who have been exposed to greater physical earthquake damage to their neighborhood than those who have experienced less damage but also live in Christchurch.Methods: Based on nearly all the residential population and clinical mood and anxiety diagnoses in Christchurch, spatio-temporal scan analysis with SaTScanTM was used to detect clusters of mood and anxiety between July 1, 2009 and June 30, 2012. Multivariate logistic regression was used to assess the relationship between those disorders and the extent of damage to the neighbourhood measured by the Canterbury Earthquake Recovery Authority (CERA) land zoning, lateral spreading, liquefaction and the hazard intensity measures Modified Mercalli Intensity (MMI) and Peak Ground Acceleration (PGA).Results: Spatio-temporal cluster analysis showed that people living in the more damaged central and eastern parts of the city have a 24% higher risk of developing a mood or anxiety disorder than people living in less damaged parts of the city. Multivariate logistic regression showed that mood and anxiety disorders increased with increasing earthquake intensity and resultant increased extent of damage. Overall, people who haven’t had a prior mood or anxiety disorder are at greater risk of developing such a disorder by living in closer proximity to more affected areas. Furthermore this suggests that living in closer proximity to areas that are more likely to suffer from damage in future earthquakes is likely to increase the risk of getting a mood or anxiety disorder in the population, and thus could benefit from targeted health interventions.

Necessity as the mother of invention: Innovative responses to natural disasters

How do innovators respond to the shock of a natural disaster? Do natural disasters spur technical innovations that can reduce the risk of future hazards? This paper examines the impact of three types of natural disasters—floods, droughts and earthquakes—on the innovation of their respective mitigation technologies. Using patent and disaster data, our study is the first to empirically examine adaptation responses across multiple sectors at the country level. Considering the potential endogeneity of disaster damages, we use meteorological and geophysical data to create hazard intensity measures as instrumental variables. Overall, we show that natural disasters lead to more risk-mitigating innovations, while the degree of influence varies across different types of disasters and technologies.