0

IN THIS ISSUE

Newest Issue


Editorial

ASME J. Risk Uncertainty Part B. 2017;3(2):020201-020201-1. doi:10.1115/1.4036240.
FREE TO VIEW

Complex engineered networks form a technological skeleton of our modern society. Examples include electric power grids, road and airline networks, cellular grids, and various distribution networks, such as water, gas, and petroleum networks. These distributed complex systems with many interconnected components provide critical services for everyday life, such as water, food, energy, transport, communication, banking, and finance. As a result of technological progress and worldwide urbanization and globalization processes, the dependence of our society on these complex systems spanning cities, countries, and even continents constantly grows. Given the critical role that engineered networks play in the functioning of our world, there is an increasing demand for these systems to be highly reliable and resilient. A deep understanding of their actual capabilities to withstand natural hazard, such as earthquakes, tsunamis, and hurricanes, and man-made threats, e.g., accidents and terrorism, is crucial. The related issues of resilient network design and operation are also closely related to sustainability problems which are of increasing importance today. In particular, the degree to which an engineered network subjected to internal or external stresses (e.g., cascading failures or seismic hazards) is capable of keeping (or recovering) the service demanded needs to be quantitatively estimated. A failure of a critical infrastructure to provide the required service could lead to a range of serious consequences for business, government, and the community. Quantitative assessment of network reliability and associated risks and uncertainties is therefore a key aspect of system design, optimization, and operation.

Commentary by Dr. Valentin Fuster

Research Papers

ASME J. Risk Uncertainty Part B. 2017;3(2):021001-021001-10. doi:10.1115/1.4035728.

To investigate the resilience of interconnected critical infrastructures (CIs), a framework combining dynamic modeling and resilience analysis is proposed. Resilience is defined in this work as the capacity of a system to absorb the impacts of perturbations and recover quickly from disruptive states. It is seen as a property of the system, which depends on a number of design, operation, and control parameters. Within this framework, we introduce the concept of resilience regions in the parameters space: as long as the parameters values remain inside these regions during operation, the system visits only recoverable states or, in other words, it maintains nominal operation or recovers quickly to it. Based on this concept, we perform a resilience analysis of two interconnected critical infrastructures, a gas network and an electric power system. The analysis is performed by numerical calculation of the resilience conditions in terms of design, operation, and control parameters values for given failure scenarios. To render computationally feasible analysis, we resort to an abstract representation of the system dynamics by a linear model of switching dynamics. Although the high-level modeling adopted may suffer from predictive accuracy, the proposed framework can still provide valuable insights in the analysis of system resilience and its dependence on the design, operation, and control parameters under different failure scenarios, which can be valuable to inform the decision making process of CIs operators and other stakeholders.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021002-021002-11. doi:10.1115/1.4035729.

Eco-Industrial parks (EIPs) and industrial symbioses (IS) provide cost-effective and environmental friendly solutions for industries. They bring benefits from industrial plants to industrial parks and neighborhood areas. The exchange of materials, water, and energy is the goal of IS to reduce wastes, by-products, and energy consumption among a cluster of industries. However, although the IS design looks for the best set of flow exchanges among industries at a network level, the lack of access to accurate data challenges the optimal design of a new EIP. IS solutions face uncertainties. Considering the huge cost and long establishment time of IS, the existing studies cannot provide a robust model to investigate effects of uncertainty on the optimal symbioses design. This paper introduces a framework to investigate uncertainties in the EIP design. A multi-objective model is proposed to decide the optimal network of symbiotic exchanges among firms. The model minimizes the costs of multiple product exchanges and environmental impacts of flow exchanges. Moreover, this paper integrates the analysis of uncertainties effects on synergies into the modeling process. The presented models are depicted through optimizing energy synergies of an industrial zone in France. The efficiency of single and multiple objective models is analyzed for the effects of the identified uncertainties. In addition, the presented deterministic and robust models are compared to investigate how the uncertainties affect the performance and configuration of an optimal network. It is believed that the models could improve an EIP's resilience under uncertainties.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021003-021003-9. doi:10.1115/1.4035730.

Brussels Airport ceased operations for 12 days after a coordinated improvised explosive device (IED) attack by suicide bombers in March 2016, demonstrating that critical transport hubs can be disrupted for significant durations by terrorists. Designers of critical infrastructure need to consider countermeasures to such attacks, reducing a target's attractiveness and improving opportunities for business continuity. This can be achieved by considering the cost–benefit of potential countermeasures during the design phase for infrastructure. This paper uses a probabilistic risk assessment (PRA) model for IED attack to assess the costs and benefits of using distributed security queuing at airport terminals. Our results demonstrate that the use of distributed security queuing will offer casualty reductions when used in preference to centralized security queuing. However, when considering the cost–benefit of introducing distributed security queuing, on the basis of a single small to medium IED attack, it is likely that implementing this countermeasure would not be deemed cost-effective from a purely financial perspective, particularly when the threat likelihood is very low.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021004-021004-14. doi:10.1115/1.4035737.

Failure or malfunction of complex engineered networks involves relevant social and economic aspects, so that their maintenance is of primary importance. In assessing the reliability of such networks, it should be duly considered that they are a whole made of different parts, and that some of these individual parts or structures are often crucial to assure the proper operation of the entire network. Moreover, each of these structures can be considered a complex system by itself: structural reliability theory should be thus combined with advanced numerical analysis tools in order to obtain realistic estimates of the probability of failure. Accurate estimations are especially required in seismic zones, aiming to efficiently plan future interventions. This paper presents a method for the reliability assessment of a critical element of engineered networks. The method is discussed with special reference to a relevant case study: a concrete water tank, which is a key component of a water supply system. Special attention is devoted to the reliability assessment of the tank under seismic loads, based on a structural identification approach. The calibration of the finite element model (FEM) of the structure is carried out on probabilistic basis, applying the Bayes theorem and response surface methods. The proposed approach allows to significantly speed up the structural identification process, leading to sounder estimate of the input parameters. Finally, the seismic fragility curves of the structure are developed according to the relevant limit states, demonstrating that information regarding the global structural behavior and local checks can be effectively combined in structural reliability assessments.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021005-021005-9. doi:10.1115/1.4035843.

Extreme weather forms a major threat to electricity distribution networks and has caused many severe power outages in the past. A reliable electrical grid is something most of us take for granted, but storms, heavy snowfall, and other effects of extreme weather continue to cause disruptions in electricity supply. This paper contributes to ensuring the continuity of electricity supply under adverse weather events. The aim is to describe and to analyze how the continuity of electricity supply can be ensured in the case of extreme weather. Based on the research, the energy sector is highly dependent on the existing locations and structures of the current infrastructure. Aging infrastructure is commonly seen as a main vulnerability factor. The most vulnerable parts of the electricity distribution system to extreme weather conditions are the networks built as overhead lines. However, the resilience of the networks against extreme weather can be increased significantly in all phases of a disaster management cycle. Methods and technological solutions proposed in this paper to alleviate such problems include adjacent forest management and periodic aerial inspections, situational awareness, distributed generation and microgrids, placement of overhead lines, underground cabling, and unmanned air vehicles. However, it must be noticed that the methods and their value for stakeholders are context-dependent. Thus, their applicability and appropriateness may change over time.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021006-021006-10. doi:10.1115/1.4036152.

Optimizing the topology of complex infrastructure systems can minimize the impact of cascading failures due to an initiating failure event. This paper presents a model-based design approach for the concept-stage robust design of complex infrastructure systems, as an alternative to modern network analysis methods. This approach focuses on system performance after cascading has occurred and examines design tradeoffs of the resultant (or degraded) system state. In this research, robustness is classically defined as the invariability of system performance due to uncertain failure events, implying that a robust network has the ability to meet minimum performance requirements despite the impact of cascading failures. This research is motivated by catastrophic complex infrastructure system failures such as the August 13th Blackout of 2003, highlighting the vulnerability of systems such as the North American power grid (NAPG). A mathematical model was developed using an adjacency matrix, where removing network connections simulates uncertain failure events. Performance degradation is iteratively calculated as failures cascade throughout the system, and robustness is measured by the lack of performance variability over multiple cascading failure scenarios. Two case studies are provided: an extrapolated IEEE 14 test bus and the Oregon State University (OSU) campus power network. The overarching goal of this research is to understand key system design tradeoffs between robustness, performance objectives, and cost, and explore the benefits of optimizing network topologies during the concept-stage design of these systems (e.g., microgrids).

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021007-021007-9. doi:10.1115/1.4036091.

We investigate reliability and component importance in spatially distributed infrastructure networks subject to hazards characterized by large-scale spatial dependencies. In particular, we consider a selected IEEE benchmark power transmission system. A generic hazard model is formulated through a random field with continuously scalable spatial autocorrelation to study extrinsic common-cause-failure events such as storms or earthquakes. Network performance is described by a topological model, which accounts for cascading failures due to load redistribution after initial triggering events. Network reliability is then quantified in terms of the decrease in network efficiency and number of lost lines. Selected importance measures are calculated to rank single components according to their influence on the overall system reliability. This enables the identification of network components that have the strongest effect on system reliability. We thereby propose to distinguish component importance related to initial (triggering) failures and component importance related to cascading failures. Numerical investigations are performed for varying correlation lengths of the random field to represent different hazard characteristics. Results indicate that the spatial correlation has a discernible influence on the system reliability and component importance measures, while the component rankings are only mildly affected by the spatial correlation. We also find that the proposed component importance measures provide an efficient basis for planning network improvements.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2017;3(2):021008-021008-15. doi:10.1115/1.4036155.

In risk analysis of rare events, there is a need to adopt data from different sources with varying levels of detail (e.g., local, regional, categorical data). Therefore, it is very important to identify, understand, and incorporate the uncertainty that accompanies the data. Hierarchical Bayesian analysis (HBA) addresses uncertainty among the aggregated data for each event through generating an informative prior distribution for the event's parameter of interest. The Bayesian network (BN) approach is used to model accident causation. BN enables both inductive and abductive reasoning, which helps to better understand and minimize model uncertainty. In this work, the methodology is proposed to integrate BN with HBA to model rare events, considering both data and model uncertainty. HBA considers data uncertainty, while BN uses an adaptive model to better represent and manage model uncertainty. Application of the proposed methodology is demonstrated using three types of offshore accidents. The proposed methodology provides a way to develop a dynamic risk analysis approach to rare events.

Commentary by Dr. Valentin Fuster
Select Articles from Part A: Civil Engineering

Technical Papers

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;2(4):. doi:10.1061/AJRUA6.0000862.
Abstract 

Abstract  Natural disasters in the past decade have encouraged agencies responsible for development and maintenance of infrastructure systems toward the accounting of risk and resilience in asset management, buying down risks to economic, environmental, and social objectives. A principal aim of continuous asset risk management is the resilience of large-scale systems. This paper adopts and describes metrics and models for asset management. This paper implements priorities for three diverse classes of assets—waterway navigation, hydropower, and flood control—and identifies key challenges for risk and resilience analytics, including data quality, variability across business lines in interpretations of risk buydown, assumptions of project synergies and interactions, and evolving agency missions and organizational structures. The scale of the demonstration is thousands of assets, US$4 billion of needs, and US$2.3 billion of available funds. Attributes representing the objectives to minimize consequential damages are elicited and alternatives ranked by their potential threat to these objectives. The various sources of model and data uncertainty are characterized, and appropriate treatments of uncertainties related to risk and resilience are recommended.

Topics:
Flood control , Risk management , Hydropower , Resilience
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;2(4):. doi:10.1061/AJRUA6.0000876.
Abstract 

Abstract  In this paper, several alternative approaches are used to implement the failure sampling method for structural reliability analysis and are evaluated for effectiveness. Although no theoretical limitation exists as to the types of problems that failure sampling can solve, the method is most competitive for problems that cannot be accurately solved with reliability-index-based approaches and for which simulation is needed. These problems tend to have nonsmooth limit-state boundaries or are otherwise highly nonlinear. Results from numerical integration and three extrapolation approaches using the generalized lambda distribution, Johnson’s distribution, and generalized extreme-value distribution are compared. A variety of benchmark limit-state functions were considered for evaluation where the number of random variables, degree of nonlinearity, and level of variance were varied. In addition, special limit-state functions as well as two complex engineering problems requiring nonlinear finite-element analysis for limit-state function evaluation were considered. It was found that best results can be obtained when failure sampling is implemented with an extrapolation technique using Johnson’s distribution, rather than with numerical integration or the generalized lambda distribution as originally proposed with the method.

Topics:
Event history analysis , Failure
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;2(4):. doi:10.1061/AJRUA6.0000877.
Abstract 

Abstract  The estimation of cascading indirect consequences of natural hazard events is possible if infrastructure managers quantify functional capacity losses (i.e., inability to provide adequate level of service) and downtimes of networks. This work presents a methodology to estimate probable functional capacity losses of individual objects consisting of a network and their corresponding probable restoration time, and relating them to various hazard intensities. Furthermore, the methodology quantitatively relates object functional capacity losses, object restoration time, network restoration sequence, network functional capacity loss, and network downtime to support the generation of network states, leading to the estimation of indirect consequences. This work also shows how the proposed functional loss assessment and restoration analysis processes may be integrated into disaster risk assessment processes that are designed to solely estimate direct consequences. Although this methodology supports the evaluation of infrastructure networks of no particular sector, illustrations are drawn primarily from the transport sector, including an example that demonstrates the application of this methodology.

Topics:
Hazards
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;2(4):. doi:10.1061/AJRUA6.0000865.
Abstract 

Abstract  Probabilistic sensitivity analysis is a crucial tool in the uncertainty analysis of systems, which allows the understanding of how the uncertainty in the output response can be apportioned to different sources of uncertainty in the input parameters. Sobol’s method is a widely accepted global sensitivity analysis (GSA) technique that has been applied to rank the input design parameters, based on their respective impact on the response randomness. Although this variance-based technique is highly efficient when the design parameters are independent, the estimation of Sobol indices in the presence of correlation has not been sufficiently documented. This paper addresses this shortcoming through the development of a generalized method for GSA in the Bayesian back-analysis framework, in which the Kullback-Leibler (K-L) entropy measure serves as the measure of sensitivity. The methodology has been explored in the context of design of flexible pavements in the mechanistic-empirical (M-E) framework, in which considerable correlation among the design parameters has been reported. The probabilistic back-analysis method has been solved using the Markov chain Monte Carlo (MCMC) simulation method to identify the critical parameters contributing to the failure of a flexible pavement section by fatigue cracking and rutting. The study shows that the sensitivity estimates for the two modes of failure are impacted by the presence of design parameter correlation. The advantage of this method over other techniques of GSA, in addition to the incorporation of correlation, is that the flexibility of the Bayesian methodology allows the incorporation of model uncertainty. The probabilistic back-analysis approach based on Bayes’ theorem thus presents a generalized method that can be efficiently used for the estimation of the critical parameters that contribute to the failure of any system.

Topics:
Sensitivity analysis , Pavement
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;2(4):. doi:10.1061/AJRUA6.0000867.
Abstract 

Abstract  Estimating the economic burden of disasters requires appropriate models that account for key characteristics and decision-making needs. Natural disasters in 2011 resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to significant savings through risk reduction and expeditious recovery. The management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics with models for examining the economics of resilience. This paper provides such microeconomic models, compares them, examines their sensitivities to key parameters, and illustrates their uses. Such models enable improving the resiliency of systems to meet target levels.

Topics:
Economics , Resilience

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In