Risk-informed Decision Research Articles

Quantitative risk assessment of a hydrogen refueling station by using a dynamic physical model based on multi-physics system-level modeling

Numerous accidents in HRSs have been reported worldwide in accident databases; therefore, many researchers have performed quantitative risk assessments (QRAs) of HRSs to enable risk-informed decision making in determining the safety distances or risk mitigation measures. The HRSs, located in urban areas such as Tokyo in Japan, are situated in congested areas with tall buildings and high population density; thus, they have relatively narrow station areas. However, the QRAs are generally suitable for large plants such as nuclear power plants or chemical plants; therefore, relatively small plants or installations, such as HRSs, have not yet been considered as QRA objects. Hence, it is necessary to conduct detailed QRAs with risk analyses and reduce the applied uncertainties for relatively small plants or installations. We applied a model-based approach of risk assessment to model the HRS process using multi-physics system-level modeling and simulated a target system using Modelica—an equation-based, object-oriented modeling language that allows acausal modeling of complex cyber-physical systems The primary aim of this study was to conduct a QRA of an HRS based on multi-physics system-level modeling. First, we modeled the HRS components and physical relationships between the components using basic physical equations. Then, we elucidate a QRA based on the constructed model. The difference in the leakage rates due to the leak positions and dynamic behavior of the model parameters were calculated using the constructed model. Finally, we estimated the individual risks of all the scenarios and compared the resulting risk contours based on the constructed model that includes the hydrogen-fuel dynamic behavior with those based on the traditional model. These results indicate that it is possible to assess whether the risks around the station boundary are acceptable based on the scenario information obtained by evaluating the risks near the station.

Global Methodology for Electrical Utilities Maintenance Assessment Based on Risk-Informed Decision Making

Modern electrical power utilities must deal with the replacement of large portions of their assets as they reach the end of their useful life. Their assets may also become obsolete due to technological changes or due to reaching their capacity limits. Major upgrades are also often necessary due to the need to grow capacity or because of the transition to more efficient and carbon-free power alternatives. Consequently, electrical power utilities are exposed to significant risks and uncertainties that have mostly external origins. In this context, an effective framework should be developed and implemented to maximize value from assets, ensure sustainable operations and deliver adequate customer service. Recent developments show that combining the concepts of asset management and resilience offers strong potential for such a framework—not only for electrical utilities, but for industry, too. Given that the quality and continuity of service are critical factors, the concept of Value of Lost Load (VoLL) is an important indicator for assessing the value of undelivered electrical energy due to planned or unplanned outages. This paper presents a novel approach for integrating the power grid reliability simulator into a holistic framework for asset management and electrical power utility resilience. The proposed approach provides a sound foundation for Risk-Informed Decision Making in asset management. Among other things, it considers asset performance as well as the impact of both current grid topology and customer profiles on grid reliability and VoLL. A case study on a major North American electrical power utility demonstrates the applicability of the proposed methodology in assessing maintenance strategy.

A risk-informed decision support tool for the strategic asset management of railway track infrastructure

The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure conditions by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget.

Safely Managed On-Site Sanitation: A National Assessment of Sanitation Services and Potential Fecal Exposure in Indonesia.

Sustainable Development Goal target 6.2 calls for universal access to adequate and equitable sanitation, setting a more ambitious standard for ‘safely managed sanitation services’. On-site sanitation systems (e.g., septic tanks) are widely used in low- and middle-income countries (LMICs). However, the lack of indicators for assessing fecal exposure risks presents a barrier to monitoring safely managed services. Furthermore, geographic diversity and frequency of disasters require a more nuanced approach to risk-informed decision-making. Taking Indonesia as an example, the purpose of this paper is to provide insights into current status and practices for on-site sanitation services in the contexts of LMICs. Using a dataset from a national socio-economic survey (n = 295,155) coupled with village census (n = 83,931), we assessed (1) household sanitation practices across Indonesia stratified by city-level population density and meteorological factors, (2) factors associated with septic tank emptying practice, and (3) inequalities in potential fecal exposure as measured by population density and WASH access by wealth quintile. We found a high reliance on on-site sanitation facilities (80.0%), almost half of which are assumed to be ‘uncontained’ septic tanks and one in ten facilities discharging untreated waste directly into the environment. The most densely populated areas had the highest rates of septic tank emptying, though emptying rates were just 17.0%, while in the lowest population density group, emptying was rarely reported. Multivariate regression analysis demonstrated an association between flooding and drought occurrence and septic tank emptying practice. Higher groundwater usage for drinking among poorer households suggests unsafe sanitation may disproportionally affect the poor. Our study underscores the urgent need to strengthen the monitoring of on-site sanitation in LMICs by developing contextualized standards. Furthermore, the inequalities in potential fecal exposure require greater attention and tailored support mechanisms to ensure the poorest gain access to safely managed sanitation services.

Sensitivity analysis of system reliability using the complex-step derivative approximation

Sensitivity analysis of system failure probability is performed to evaluate the dependence of system reliability on design parameters or component events. Sensitivity analysis is integral for the implementation of efficient gradient-based optimization algorithms in system reliability-based design optimization, and for risk-informed decision-making. This paper presents a method for sensitivity analysis of system failure probability using complex-step differentiation. Many derivative approximations use a small step size to minimize subtractive cancellation errors. The complex-step approximation utilizes an imaginary number, such that the subtractive cancellation is not included in the formulation, resulting in calculations without the associated round-off error. The level of accuracy in sensitivity analysis using the finite difference method (FDM) can vary with change in the step size, which is generally selected arbitrarily, as the actual effect of the step size on the result itself is difficult to predict prior to actual calculation. The complex-step approximation, however, is not confined by the step size, as subtraction cancellation is not included. Compared to the FDM, the complex step approximation has only one limit, the numerical precision of evaluating the function. The proposed method integrates complex-step differentiation into a numerical integration scheme, for the assessment of system reliability and sensitivity. System failure probability and sensitivity are obtained by taking the real and imaginary part of the cumulative distribution function, which is numerically evaluated using the proposed method. The computational efficiency for system reliability problems involving high dimensionality is improved with the utilization of a dimension reduction technique. Numerical examples of sensitivity analysis for series, parallel, and general systems are presented to illustrate the performance of the proposed method.

An alternative quantification of the value of information in structural health monitoring

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

High-resolution flood risk approach to quantify the impact of policy change on flood losses at community-level

Communities are changing the way they manage flood risk as a result of climate change which increases both the flood hazard intensity and frequency and therefore results in more flood exposure for the built environment. Quantifying the feasibility of policy changes requires a standardized approach that can allow a risk-informed decision. The current body of flood risk mitigation research is populated with a host of qualitative approaches to reduce flood risk at the building- and community-level. However, quantitative effects on physical infrastructure within the community at a detailed level, i.e. component- and building-level, are needed to provide decision-support for policymakers. In this study, a high-resolution quantitative approach is developed to determine the effect of policy change on flood losses at the community-level by integrating building information models with the community topology. This approach uses a portfolio of building archetypes to model flood vulnerability at the community-level. For each archetype within this portfolio, a systematic approach was developed to propagate uncertainties in the flood damage for each component within the building and thereby providing the total building damage in terms of exceedance probability of a set of prescribed damage states. The developed approach was used to investigate an array of community-level mitigation options that could control both the flood hazard and flood exposure. The analysis of an example community revealed that one mitigation policy is not enough to provide efficient flood protection and a suite of diverse mitigation techniques working in concert is more effective for major flood protection.

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. The MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

Comparative Performance Evaluation of HEC-HMS and SWAT Models in Stream Flow Simulation: the Case of Bilate and Gidabo Watersheds, Ethiopia

Many hydrological models have been developed to simulate watershed hydrology. However, identifying the most cost-effective and efficient hydrological models for a specific watershed with reasonable certainty becomes difficult. The purpose of this study was to compare the stream flow prediction efficiency of the HEC-HMS and SWAT models, as well as the associated uncertainty, in the Bilate and Gidabo watersheds. Model-sensitive parameters being identified, they were calibrated and validated. The parameter uncertainties were analyzed using Markov Chain Monte Carlo (MCMC) for HEC-HMS and Sequential Uncertainty Fitting version two (SUF-2) for SWAT. In the case of the HEC-HMS model, the results showed that constant loss rate (CR) was the most sensitive parameter, followed by lag time (LT) for both watersheds. SWAT detected ALPHA_BF in the Bilate Watershed and CN_2 in the Gidabo Watershed as the most sensitive parameters. Overall, both models could adequately simulate the hydrology of both watersheds. Despite their similar modeling capabilities, a comparison analysis revealed that the HEC-HMS model outperformed the SWAT model in simulating streamflow in both watersheds. The findings of this study can help potential model users make risk-informed decisions by selecting a representative model and quantifying associated uncertainty in the modeling field.

ENHANCING FORECAST-BASED DISASTER RESPONSE IN NEPAL

Implementation of humanitarian actions in advance of a disaster event is a new approach to enhance overall effectiveness of disaster responses. Early actions following forecasts and early warnings can significantly reduce disaster losses and the cost of disaster recovery. Evidence from pilot projects reveal potential to integrate forecast-based humanitarian actions into disaster preparedness planning. Building on advanced technologies, it has been possible to predict disaster risk of many meteorological and hydrological hazards like heavy rainfall, storm surges, flood, drought and cyclones. Nepal has developed communitycentered, end to end flood early warning systems, which have utilised different global and regional weather forecasting models. The models have the capability to provide weather and flood scenarios three days in advance. In this study, we carefully examine current practices and approaches to explore opportunities to use weather forecasts, flood alerts and warning to inform forecast-based humanitarian actions. Furthermore, we analysed existing policy provisions and legal mandates in Nepal to assess the availability of enabling environment needed for adopting forecast-based humanitarian actions. We also present our learning from piloting this approach to disaster preparedness planning in 19 selected districts of Nepal. Our findings suggest that adequate legal provisions and appropriate institutional mechanisms are essential to ensure effective implementation of forecastbased early actions. It is important and urgent to depart from traditional post-event relief approach to a risk-informed preventive decision-making. Technological limitations and operational gaps between agencies are major barriers to proactive actions. The challenges can be overcome through sufficient legal provisions, technical guidelines and protocols to clarify roles, responsibilities and accountabilities of the authorities.

Probabilistic Risk Assessment of Coupled Natural-Physical-Social Systems: Cascading Impact of Hurricane-Induced Damages to Civil Infrastructure in Galveston, Texas

The combined effect of storm surge and wave action during severe storms in coastal regions can cause significant damage to civil infrastructures with cascading consequences to coastal communities and their residents with respect to emergency response, repair, and recovery. This coupling of natural, physical, and social systems presents an important yet relatively underexplored problem in the probabilistic risk assessment of coastal systems. Not only does coupling exist among the built and social systems triggered by natural hazard events, but a wealth of sources of uncertainties inherent in modeling these systems also renders the problem more complex. This paper presents a framework for the probabilistic risk assessment of coupled natural-physical-social systems exposed to coastal storms. It departs from traditional literature in this area by considering interconnected coastal transportation and residential building infrastructure coupled with social systems—households in this case—focusing on households failing to respond to official calls to evacuate. A holistic multihazard risk assessment framework is posed for probing these coupled systems in the face of uncertainty. New hybrid risk metrics across built and social systems are proposed, including the probability of nonconnectivity to emergency services, time loss to access emergency services, and the number of nonevacuees at risk. The concept of a hot household is developed, at which a building with nonevacuees inside experiences collapse-limit failure and is disconnected from emergency services during a storm event. The proposed framework is applied to a case study on Galveston Island, Texas, considering uncertainties in storm frequency and intensity, damage to physical infrastructure (buildings, roadways, and bridges), and evacuation decisions of the population. The resulting models offer a foundation for risk-informed decision-making to enhance the resilience of coastal communities and provide new insight into the coupled performance of natural-physical-social systems in the face of coastal storms.

DIGITAL NUCLEAR LEADERSHIP FOR SAFETY AND SECURITY- TOWARDS EFFECTIVE REGULATORY CONTROL

The revaluation of information technologies nowadays encourages the reliance on Digital Leadership within the business environment using innovative solutions and digital technologies. National Regulators for safety, security and safeguard face huge challenges to meet this specific mandated and establish an optimized regulatory practice for nuclear and radiation facilities and activities. Digital leadership plays a big role to highlight the area of the interface, identify opportunities, enhance organization culture, and encourage risk-informed decisions. This work introduces the Integrated Tool for Leadership and Knowledge Management for Safety and Security (ITLKMSS). It is tailed with specific requirements to create an automated infrastructure with five modules; Knowledge Management portal, Human Resource Development, and Training Centre, Sarcoma-Plus, Staff Information Database, and e-Learning Portal. The tool is developed and tested In-house for a planned scenario to measure its effectiveness. Results support the national efforts for digitalization and help to grantee sustainability of the regulatory framework with effective performance in a challenging context.

Maximum likelihood estimation of probabilistic non-suppression model for OECD NPP electrical fire applying non-negative continuous distribution

The securement and improvement of realism in a probabilistic fire risk assessment (Fire PRA) are important in risk-informed performance-based regulations and decision support. In this study, a probabilistic fire brigade non-suppression model is developed for electrical fires using the Organization for Economic Cooperation and Development fire incident data on operating nuclear power plants collected from various countries by applying a non-negative continuous probability distribution with the maximum likelihood estimation method. The result of fitting 15 types of a non-negative continuous probability distributions shows that the log-normal probability model is the best fitting and most adequate model, and can best represent the actual fire suppression time by a fire brigade. The selected log-normal probability model was compared with the exponential probability model being used in an existing Fire PRA, which shows that the level of adequacy of the log-normal probability model is improved with a decrease in the bayesian information criteria by 7.9%, residual sum of squares by 100.0%, and mean squared error by 57.6%. The log-normal probability model selected from this study is expected to contribute to an enhancement of the Fire PRA realism in support of risk-informed decision making by reflecting actual fire suppression experience.

Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions.

In the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning through associations, which answers questions such as: “How does new evidence x′ about the occurrence of event X change my belief about the occurrence of event Y?” Associative reasoning has helped risk analysts to identify relevant risk‐contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessment has yet to explore other features of BNs, such as the ability to reason through interventions, which enables the BN model to support answering questions of the form “How does doing X=x′ change my belief about the occurrence of event Y?” In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk‐informed decision support by enabling the modeling of policies and actions before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third‐party damage of natural gas pipelines, showing how BNs can be used to inform decision‐makers about the effect that new actions/policies can have on a system.

Discussion on the treatment of various dependencies in a time-dependent risk monitoring model of redundant systems

This paper investigates the time-dependent modeling of the operating behaviors of systems in risk monitoring applications. The approach used focuses on the constructed concept of the risk monitoring time axis and on time-dependent failure probability modeling for dependencies based on this axis. Dependencies among the complex operating behaviors in nuclear power plants (NPPs) are often superimposed on each other. To ensure the safe and reliable operation of the system, a large number of redundant designs are used in NPPs. In this paper, we mainly focus on the dependencies that are common in redundant systems, such as state dependencies, sequential failures and common-cause failure (CCF). Studies are conducted on the rationality and applicability to the risk monitoring model of the model simplification assumptions for ignoring certain dependency problems in traditional static probabilistic safety assessment (PSA) modeling. Time-dependent mathematical models considering these dependencies in risk monitoring are proposed in this paper. Additionally, time-dependent mathematical models considering the superposition of these dependency problems in risk monitoring are developed, and the process of updating the above models with time and configuration changes is also studied in this paper to provide modelers with guidelines for representing such aspects in the risk monitoring application. Moreover, based on the above research results, a second-order redundant system is taken as an example to calculate and compare time-dependent system risk measurements of the model proposed in this paper and the time-independent system risk of the traditional risk monitoring model. These comparative studies demonstrate the effectiveness and benefits of the method proposed in this paper and provide some insightful conclusions. Only by accurately considering the time dependence and system dependencies can we obtain an accurate system risk and then make a reasonable and credible risk-informed decision.

Steep Creek Risk Assessment for Pipeline Design : A Case Study From British Columbia, Canada

ABSTRACT Pipelines in mountainous terrain often cross alluvial fans formed by steep creek processes of debris flows and debris floods and are thus exposed to their associated hazards. The design of new pipeline infrastructure and maintenance of existing pipelines necessitates steep creek risk assessments and appropriate mitigation design. We present methodology for assessing steep creek risk along pipeline routes that evaluates the probability of such processes causing a pipeline loss of containment or disruption in service. The methodology consists of estimating event frequency, scour potential, and the vulnerability of the pipeline to break if impacted by boulders. The approach can be adapted to other landslide geohazards so that different geohazard locations can be evaluated with a common metric. Steep creek process frequency is estimated based on field observations and review of documented events, historical air photo records, and terrain mapping based on LiDAR-generated topography. Scour potential is estimated based on channel morphology, presence of bedrock, and grain size distribution of channel bed material. Vulnerability is estimated based on flow width and velocity and can be modified for different pipe diameters and wall thicknesses. Mitigation options for buried pipelines include those intended to decrease the likelihood of the pipeline being exposed and to increase the resiliency of the pipeline to boulder or organic debris impacts, if exposed. The methodology presented is embedded in risk-informed decision making where pipeline owners and regulators can define probability thresholds to pipeline exposure or rupture, and pipeline designers can demonstrate that proposed mitigation measures achieve these threshold criteria.

Practical Method of Risk-Informed Decision Making in Nuclear Power Plant Operation and Its Safety Effect

Practical Method of Risk-Informed Decision Making in Nuclear Power Plant Operation and Its Safety Effect

An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard

Housing infrastructure is a basic human need and thus should be resilient against natural disasters. Therefore, an effective resilience-based framework for housing infrastructure is required to enhance the endurance capacity of housing and for effective recovery. Involving an expert opinion is also required for the representation of the nonlinear, complex relationship between the flood resilience parameters for housing infrastructure. In this work, initially, a hierarchical flood resilience model for housing infrastructure is developed using the decision-making trial and evaluation laboratory (DEMATEL) and the interpretive structure modeling (ISM) methods. The relationships among various resilience parameters related to flood hazards are obtained using integrated DEMATEL and ISM following the data received from expert opinions. Next, a flood resilience-based decision-making framework is developed integrating DEMATEL, ISM, and Bayesian Network (BN) methods. A field survey collects all the postdisaster data to feed in the BN model, capturing the interrelationship among the resilience parameters. Finally, the developed framework is implemented for a community in northeast India for flood resilience assessment and risk-informed decision making. It has been observed from the result that the resilience of the housing infrastructure for most of the surveyed places are extremely low, which means houses of those areas should be immediately strengthened. The evaluated resilience values provide the vulnerable scenarios of housing infrastructure of that community against flood hazards, which help the public authority of that community. Additionally, the identification of the most sensitive parameter/s among all considered parameters helps in decision making for future hazards.

Supervised dynamic probabilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results

One challenge that has received attention in maritime industry is assessing the risk level of dynamic positioning (DP) systems in emergency situations. Statistics from recent years have shown that the risk level of some DP operations is above the industry's risk criteria. Operators have a significant impact on incidents’ consequences by making responsive decisions. In emergencies, one is afforded little time to make a decision. Available risk models are not efficient enough to provide systems’ risk level in a short period of time.In this study, the application of a new supervised methodology to assist decision making in emergencies is proposed. This method significantly reduces the processing and execution time of a system's probabilistic risk assessment models. In this methodology, the most probable failure scenarios are generated using an optimization model. The objective of the optimization model in this study is to find scenarios with the highest occurrence probabilities. The constraints are a system's dynamic simulation and its risk model. The proposed method is applied to three incidents that occurred in the Norwegian offshore sector in previous years. The results show that the model can predict the most probable scenarios with an acceptable accuracy in a very short time.

Risk-based assessment of the post-earthquake functional disruption and restoration of distributed infrastructure systems

A risk-based framework for assessing the post-earthquake functional loss and restoration of distributed infrastructure systems is presented. Using an appropriate seismic source model, a stochastic earthquake catalogue is generated for the target region. For each event in the catalogue, the joint distribution of shaking and component-level damage maps are produced. Process-based discrete event simulation is coupled with an appropriate method for linking physical damage to functional impacts to model the restoration for each damage map. By considering all possible events that are impactful to the target region and their associated rates of occurrence, the full probability distribution of the functional recovery trajectory of the system is obtained. Dispersion disaggregation is used to isolate the individual contribution of each random variable to the uncertainty bounds in the restoration trajectory. A case study involving the city of Napa's water system is used to demonstrate how the framework can support resilience-based risk-informed decision-making for distributed infrastructure.