Infrastructure Risk Assessment Research Articles

Cross-entropy-based Adaptive Importance Sampling for Probabilistic Seismic Risk Assessment of Lifeline Networks Considering Spatial Correlation

There exist pressing research needs for developing sophisticated methodologies for probabilistic seismic risk assessment (PSRA) of complex lifeline networks, which are critical backbones of today's urban communities. In particular, ground-motion intensities at different locations of lifeline networks show significant variability and spatial-correlation, which should be properly considered in evaluating structural damage of lifeline networks and their corresponding network-level performance. In addition, the probabilities of the post-disaster states of the network often shows large variability, so simulation-based approaches may require a huge number of computational simulations if the set of the possible network states includes rare events. To improve the efficiency of simulation-based approach while maintaining its broad applicability, adaptive importance sampling methods have been recently introduced. In this study, a new PSRA approach is developed for lifeline networks by extending a cross-entropy-based adaptive importance sampling (CE-AIS) to estimate the probabilities of multiple possible states of the network utility concurrently. To test and demonstrate the proposed approach, a hypothetical traffic network under seismic hazard from surrounding active faults is investigated. The results of the numerical example show that the proposed approach, termed as CE-based concurrent AIS (CE-CAIS), identifies a near-optimal sampling density through only a few rounds of pre-sampling and drastically improves efficiency of simulation-based PSRA. It is also noted that CE-CAIS does not rely on a subjective intuition to select importance sampling density or require additional reliability analysis to identify important regions. The proposed method is expected to improve PSRA of lifeline networks in terms of efficiency and applicability, and provide new insights into the risk assessment and management of such urban infrastructures.

A decision model for intergenerational life-cycle risk assessment of civil infrastructure exposed to hurricanes under climate change

Public awareness of civil infrastructure performance has increased considerably in recent years as a result of repeated natural disasters. Risks from natural hazards may increase dramatically in the future, given current patterns of urbanization and population growth in hazard-prone areas. Risk assessments for infrastructure with expected service periods of a century or more are highly uncertain, and there is compelling evidence that climatology will evolve over such intervals. Thus, current natural hazard and risk assessment models, which are based on a presumption of stationarity in hazard occurrence and intensity, may not be adequate to assess the potential risks from hazards occurring in the distant future. This paper addresses two significant intergenerational elements – the potential impact of non-stationarity in hazard due to climate change and intergenerational discounting practices – that are essential to provide an improved decision support framework that accommodates the needs and values of future generations. The framework so developed is tested through two benchmark problems involving buildings exposed to hurricanes.

Nonlinear dynamic analysis and seismic fragility assessment of a corrosion damaged integral bridge

Purpose In this paper the impact of corrosion of reinforcing steel in RC columns on the seismic performance of a multi-span concrete integral bridge is explored. A new constitutive model for corroded reinforcing steel is used. This model simulates the buckling of longitudinal reinforcement under cyclic loading and the impact of corrosion on buckling strength. Cover concrete strength is adjusted to account for corrosion induced damage and core concrete strength and ductility is adjusted to account for corrosion induced damage to transverse reinforcement. This study evaluates the impact which chloride induced corrosion of the reinforced concrete columns on the seismic fragility of the bridge. Fragility curves are developed at a various time intervals over the lifetime. The results of this study show that the bridge fragility increases significantly with corrosion. Design/methodology/approach This paper firstly evaluates the impact which chloride induced corrosion of the columns has on bridge fragility. Finally, fragility curves are developed at various time intervals over the lifetime of the bridge. The results of this study show that the bridge fragility increases significantly with corrosion. Findings 1) It was found that columns dominate the system fragility at all levels of deterioration. Therefore, it highlights the importance of good column design in terms of both seismic detailing and durability for this integral bridge type. 2) In terms of foundation settlement coupled with corrosion, it was found that settlements on the order of the discrete levels adopted for this study increased the system fragility at the slight, moderate and extensive damage states but their impact at the complete damage states is negligible. 3) Ageing considerations are currently neglected in widespread regional risk assessment and loss estimation packages for transport infrastructure. The result of this study provides a methodology that enables bridge managers and owners to employ in seismic risk assessment of existing aging bridges. Originality/value The modelling technician developed in this paper considers the impact of detailed corrosion damaged of RC column on nonlinear dynamic response and fragility of a corroded integral bridge under earthquake loading. The current modelling technique is the most comprehensive 3D fibre element model for seismic analysis and risk assessment of corroded bridges.

A major infrastructure risk-assessment framework: Application to a cross-sea route project in China

This paper proposes a major infrastructure risk assessment framework (MIRAF) based on an adapted Analytic Hierarchy Process (AHP) risk assessment model, and applies it to a cross-sea route project that is expected to connect Guangdong Province and Hainan Island. Two alternative schemes for the cross-sea route namely the tunnel scheme and the bridge scheme, are compared in terms of risks during their different project time spans. Results indicate that the risk of the bridge scheme is larger than that of the tunnel scheme, and that the risk will increase over time. Several risk factors, including damage to commercial interests of local fishermen, damage to habitat for rare and endangered animals, financial crisis and sea storm surge, are identified as significant factors during the implementation of the tunnel scheme. This approach can be used as a decision tool to identify, analyze and assess the risks existing in the major infrastructure projects.

Risk assessment and attack graph generation for collaborative infrastructures: a survey

Collaborative infrastructures allow physical, cyber and human elements to be combined and can integrate legacy systems with new technology. As organisations globally continue to take advantage of the benefits collaboration affords, it is vital that the methods for defending these systems do not fail due to societal dependence upon the assets of these critical infrastructures. Risk assessment methods and attack graphs provide platforms that facilitate modelling, detection and analysis of these collaborative networks. They endeavour to provide functionality to identify potential vulnerabilities within these large infrastructures, which potentially could leave systems exposed. Current methods are inadequate despite increased research and development. Existing schemas failings include accurate identification of relationships and interdependencies between risks and reduction of attack graph size and generation complexity. In this paper, we present a critical analysis of methods and tools which were developed to assist with risk assessment and attack graph generation within large networked environments.

Risk assessment and management for interconnected critical infrastructure systems at the site and regional levels in California's Sacramento-San Joaquin Delta

This article summarises research-in-progress for improved risk assessment and management (RAM) of critical infrastructures that interconnect across California's Sacramento-San Joaquin Delta. The need for improved RAM is patent in the Delta as elsewhere: a 'patch and pray' stalemate has developed which focuses on short-term reactive marginal maintenance and emergency response and recovery systems, all pushing infrastructures - and their engineers, designers and operators - increasingly to their performance edges and beyond. The research focuses on water supply, transportation, energy and flood protection systems, all of which are embedded in a dynamic ecosystem and showing clear signs of deterioration. Provisional findings of research activities are discussed. This article addresses critical infrastructure modelling uncertainties and ways to better understand, reduce or otherwise accommodate human/organisational and informational uncertainties in any RAM focused at the interconnected critical infrastructure system level.

Hazard and Risk Potential of Unconventional Hydrocarbon Development-Induced Seismicity within the Central United States

AbstractUnconventional hydrocarbon development-induced seismic hazard in historically aseismic regions is more frequent and concentrated than seismicity in established tectonic high-hazard zones, and the current standard of practice for risk assessment for infrastructure is not applicable for this highly variable, induced hazard. A substantial seismic increase has been observed in historically aseismic regions and in close proximity to federal infrastructure within Arkansas, Kentucky, Missouri, Oklahoma, Tennessee, Texas, and Virginia. Seismological events M2.0 and greater, spanning February 8, 1950 until October 20, 2013 were analyzed to identify and assess the hazard potential. Geospatial and temporal observations correlate the seismic increase to the rise of unconventional hydrocarbon development, wherein all production components contribute to weakening of the subsurface and induced seismicity. Unconventional hydrocarbon production hazard has become more analogous with deep ore mining in terms of ener...

Probabilistic Risk Assessment of Oil and Gas Infrastructures for Seismic Extreme Events

Modern economy and society are constantly highly dependent on a variety of critical infrastructures (CIs) over the last decades. However an observation and review of the extreme events that occurred during the last two decades reveal that while the interdependencies between the CIs are growing and getting more complex there is an increasing gap between the growing risk and the actual preparedness. Therefore, there is an utmost importance to ensure reliable and robust performance of critical infrastructures on a continuous basis, particularly during and after the occurrence of extreme events. A decision support tool for decision makers to appraise and mitigate the risk of CIs after the occurrence of seismic events is developed. The methodology analyzes the damage of critical infrastructure components by Fault-Tree-Analysis, Decision Trees and Fragility Curves. Though the methodology is suitable for a variety of critical infrastructures, this study will focus on critical Oil and Gas Network Systems, which are vital to the energy supply infrastructure of Israel. In order to assess the risk that Oil and Gas Critical Infrastructures are exposed to in case of seismic extreme events, fragility curves are derived and adjusted to different components of the Oil and Natural Gas systems. Subsequently, a variety of possible seismic scenarios are examined and analyzed in order to determine the damage of the components. The overall expected damage of the Oil or Gas system is assessed by considering the damage state of all components of the system. The expected damage states of the components disclose and emphasize the most vulnerable parts of the system. Also discussed are guiding principles for decision makers for risk informed mitigation on benefit-cost-ratio analysis.

An applied complex systems framework for risk-based decision-making in infrastructure engineering

Infrastructure networks are complex, distributed, socio-technical systems whose study requires the integration of several domains of knowledge and techniques to coherently capture key features of their constituent components (e.g., fragility), the dynamics of such networked components, and their behavior as affected by human use and operation. This paper introduces an integrative, applicable framework of Complex Distributed Agent Networks (CoDAN) for infrastructure risk assessment and management. CoDAN detects sub-systems at multiple geographical scales and associates decision units (agents) to them in the context of a decision problem. Agents use advanced optimization and risk assessment methods for decision support, and allow to model decentralized yet coordinated decisions about performance and risk management (e.g., maintenance or restoration), mimicking behavior and system response observed in practice. The application of CoDAN to illustrative examples shows the potential of the framework to unravel the complex evolution of infrastructure network performance under hazard exposure, and to reduce the computational burden of tasks associated to risk assessment and management, as compared to traditional centralized formulations.

Areas vulnerable to natural disasters and damage estimation of infrastructure in Busan, Korea

Since the damages caused by disasters associated with climate anomalies and the diversification of the social structure increase every year, an efficient management system associated with a damage assessment of the areas vulnerable to disasters is demanded to prevent or mitigate the damages to infrastructure. The areas vulnerable to disasters in Busan, located at southeastern part of Korea, were estimated based on historical records of damages and a risk assessment of the infrastructure was performed to provide fundamental information prior to the establishment of the real-time monitoring system for infrastructure and establish disaster management system. The results are illustrated by using geographical information system (GIS) and provide the importance of the roadmap for comprehensive and specific strategy to manage natural disasters.

Network-based risk assessment of the US crude pipeline infrastructure

The purpose of this project was to assess the nation’s US crude oil pipeline network and identify the hubs that would have the greatest financial impact if their operation were disrupted by a terrorist attack, natural disaster, or other catastrophic event. The resilience of the crude oil pipeline network was analysed using the model-based risk analysis (MBRA) software tool developed by the Naval Post Graduate School Center for Homeland Defense and Security. Results identified five critical hubs, with the most critical hub being the Cushing, OK Trading Hub (COTH). A disruption in operation of the COTH or any of the five critical hubs would have far reaching negative consequences, creating long lasting political, psychological, and economic impacts. Based on the results of this assessment, it is recommended that the critical hubs be considered critical national assets and protected as such.

Development Strategy of Infrastructure Risk Assessment Based Disaster Response and Management System

Major infrastructures such as levee, dam, bridge, road, etc. have a significant meaning in that they are the means to support ordinary social and industrial activities and also protect human and physical assets at the occurrence of disaster. Especially, integrated risk management of SOC facilities provides the policy maker in charge of SOC facility safety and related disaster prevention with a reasonable means of decision making and ultimately contributes to improving the safety of the applicable SOC facility. Furthermore, a variety of issues raised in the Sewol ferry disaster that occurred recently in Korea, such as, a proper response method and the relevant Golden time decision making at the occurrence of disaster, rapid grasping and reporting the situation on disaster sites, management and mobilization of resources for rescue and recover, loss of integrated control tower function, etc., might occur as well in facility disaster management. As the method of solving such issues adequately, this study suggests a strategy of new disaster response and management system. The analyses of Hazus-MH (FEMA) and DIMSuS (Purdue University) were comprehensively conducted, and based on the results required functions of new disaster response system of major infrastructure and its development directions were suggested.

Failure risk assessment of interdependent infrastructures against earthquake, a Petri net approach: case study—power and water distribution networks

Metropolitan areas consist of complicated systems of interconnected infrastructures that are highly interdependent. Disruption of one infrastructure may induce disruption in other interconnected ones. The results from analysis of one infrastructure as an independent system are not realistic without considering the behavior of other interconnected infrastructures. Consequently, the study of the interdependencies among critical infrastructures is important for addressing the cascading effects of a failed infrastructure on the entire network to properly model its performance and help the disaster management team in decision making. In this study, the extended Petri net and Markov chain have been used to demonstrate the power and water infrastructure interdependency with a case study of one of the municipal districts of metropolitan Tehran, the capital of Iran. In this research, three cases have been assessed quantitatively: (1) the intra-dependency effects of different components in each network, (2) the interdependency effects between the considered critical infrastructures and (3) the behavior of the water network considering intra- and interdependency, when the power network fails. The analyses show that considering the mentioned interdependencies has a major influence on their performance simulations and assessment of their exact vulnerability. It is concluded that the failure probability of the water network that is dependent on the failed power network is 1.66 of the independent water network in the studied region. Eventually, the results of the research could be used in design, restoration and disaster management planning for safety assessment of critical infrastructures.

An Integrated Approach for Risk Assessment of CO2 Infrastructure in the COCATE Project

An innovative risk analysis model has been developed in order to quantify and analyse safety risks related to loss-of- containment scenarios in the pipeline transport of CO2. The risk model integrates the identified failure modes, consequence estimates and emergency response, producing consistent risk profiles based on complete outcome spaces and for different system design choices. The method involves integration in a Bayesian Belief Network (BN) of analytical equations for gas dispersion combined with statistics and expert estimates of particularly uncertain variables. Future failure initiators, scenarios and impacts are captured in a graphical structure which represents and calculates the effects of common causes. The test case for the integrated risk model will be a large CO2 capture and transport network at the Le Havre industrial zone with export to Rotterdam. The primary relative advantages of the BN risk model approach are discussed.

A Compendium of Best Practices in Asset Management

The increased scientific knowledge of the management of water infrastructure, is forcing the application of radical and innovative techniques, that goes beyond seen pipes, tanks and pumps as infrastructure in the ground, but as assets which have an operational life and effective and efficient use of these component impacts on our ability as water providers and utilities to continue to provide high quality, high assurance water and sanitation services at reliably and affordably. They are now being seen as the lifeblood of the community, since they protect public health and they insure that local economies and national economies continue to run.Mounting evidence internationally suggests that the integrity of drinking water and wastewater infrastructure is at risk without a concerted effort to improve the management of key assets--pipelines, treatment plants, and other facilities - and a significant investment in maintaining, rehabilitating, and replacing these assets.The application of asset management principles in the global water industry is of particular interest at the present time due to pressures from governments, regulators, shareholders and consumer groups in many countries to provide cost-effective and sustainable water services at least cost to customers and the environment. Recognising that the application of good science and innovation was on a rapid incline, it was deemed necessary to capture these novel and creative applications, techniques and process which were contributing to this new area of importance.This Compendium of Best Practices in Asset Management demonstrates the advancements and progress being made in applying innovative and novel techniques and processes in the management of water and sanitation infrastructure. The cases highlight examples in different countries on strategic initiatives at the highest level, through the development of the policy and legislation towards ensuring that asset management becomes a legal requirement for all water services providers; innovation techniques for infrastructure risk assessment; decision- making techniques for capital investments; studies on the implementation of Asset management in utility practice, the use of GIS and IT technology, and the various little techniques of meter replacement, pressure management, continuous leak detection and rehabilitation of infrastructure.This title belongs to GWRC Report Series.ISBN: 9781780404622 (eBook)

Assessment of Seismic Risk and Importance Measures of Interdependent Networks using a Non Simulation-Based Method

Infrastructure networks are critical backbones of urban society that support virtually every economic and societal activity. Recent events have shown that these systems are susceptible to natural and man-made hazards such as earthquakes, hurricanes, and terrorist attacks, and have the potential risk of creating cascading failures due to their interdependent nature. Therefore, analyzing the vulnerability of these complex interacting infrastructures under potential hazards is critical for the development of risk mitigation strategies and effective response and recovery. To assess the seismic risk on the infrastructure, non simulation-based methods have the merit of rapid risk assessment for infrastructures and the associated decision making, because they are efficient in estimating sensitivities and conditional probabilities which are often used for identifying critical components. This article presents a non simulation-based method to assess the system reliability of complex interdependent infrastructure networks under seismic hazards. Also proposed are methods to compute importance measures to gauge the relative contribution of components to system reliability. A case study is presented to demonstrate the proposed approach that can efficiently estimate the reliability of a network system, the effect of interactions between two networks, and the importance measures of components.

Medical Mitigation Model: Quantifying the Benefits of the Public Health Response to a Chemical Terrorism Attack

The Chemical Terrorism Risk Assessment (CTRA) and Chemical Infrastructure Risk Assessment (CIRA) are programs that estimate the risk of chemical terrorism attacks to help inform and improve the US defense posture against such events. One aspect of these programs is the development and advancement of a Medical Mitigation Model-a mathematical model that simulates the medical response to a chemical terrorism attack and estimates the resulting number of saved or benefited victims. At the foundation of the CTRA/CIRA Medical Mitigation Model is the concept of stock-and-flow modeling; "stocks" are states that individuals progress through during the event, while "flows" permit and govern movement from one stock to another. Using this approach, the model is able to simulate and track individual victims as they progress from exposure to an end state. Some of the considerations in the model include chemical used, type of attack, route and severity of exposure, response-related delays, detailed treatment regimens with efficacy defined as a function of time, medical system capacity, the influx of worried well individuals, and medical countermeasure availability. As will be demonstrated, the output of the CTRA/CIRA Medical Mitigation Model makes it possible to assess the effectiveness of the existing public health response system and develop and examine potential improvement strategies. Such a modeling and analysis capability can be used to inform first-responder actions/training, guide policy decisions, justify resource allocation, and direct knowledge-gap studies.

An Approach for Assessing Risk of Common Cause Failures in Critical Infrastructures

This paper presents the technique for the critical infrastructure (CI) risk assessment based on Failure Modes, Effects and Criticality Analysis (FMECA), modified for multiple failures’ criticality assessment. The multiple failures (MFs) are significant contributors to risk in critical infrastructure. In spite of the low frequency of multiple failures’ occurrence, the severity of their consequences could lead directly to the CI’s accident and malfunctions. The influences of multiple failures should be taken into consideration as early as possible at the design stage. The paper presents classification of MFs, their root causes and coupling factors that stipulate the common susceptibility of systems to shared cause. The common cause failures (CCFs) are a subset of the dependant multiple failures. The qualitative procedure developed in the paper considers the consequences’ severity of CCFs on different I&C system levels. The total severity of CCFs is presented as a sum of severities for each level. The results of FMECA for single independent failures are taken as initial data to perform FMECA for MFs.

Spatial risk assessment for critical network infrastructure using sensitivity analysis

The presented spatial risk assessment method allows for managing critical network infrastructure in urban areas under abnormal and future conditions caused e.g., by terrorist attacks, infrastructure deterioration or climate change. For the spatial risk assessment, vulnerability maps for critical network infrastructure are merged with hazard maps for an interfering process. Vulnerability maps are generated using a spatial sensitivity analysis of network transport models to evaluate performance decrease under investigated thread scenarios. Thereby parameters are varied according to the specific impact of a particular threat scenario. Hazard maps are generated with a geographical information system using raster data of the same threat scenario derived from structured interviews and cluster analysis of events in the past. The application of the spatial risk assessment is exemplified by means of a case study for a water supply system, but the principal concept is applicable likewise to other critical network infrastructure. The aim of the approach is to help decision makers in choosing zones for preventive measures.

Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data

Infrastructure disaster risk assessment seeks to estimate the probability of a given customer or area losing service during a disaster, sometimes in conjunction with estimating the duration of each outage. This is often done on the basis of past data about the effects of similar events impacting the same or similar systems. In many situations this past performance data from infrastructure systems is zero-inflated; it has more zeros than can be appropriately modeled with standard probability distributions. The data are also often non-linear and exhibit threshold effects due to the complexities of infrastructure system performance. Standard zero-inflated statistical models such as zero-inflated Poisson and zero-inflated negative binomial regression models do not adequately capture these complexities. In this paper we develop a novel method that is a hybrid classification tree/regression method for complex, zero-inflated data sets. We investigate its predictive accuracy based on a large number of simulated data sets and then demonstrate its practical usefulness with an application to hurricane power outage risk assessment for a large utility based on actual data from the utility. While formulated for infrastructure disaster risk assessment, this method is promising for data-driven analysis for other situations with zero-inflated, complex data exhibiting response thresholds.