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Editorial

ASME J. Risk Uncertainty Part B. 2017;3(2):020201-020201-1. doi:10.1115/1.4036240.
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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. 2015;3(2):. doi:10.1061/AJRUA6.0000841.
Abstract 

Abstract  In stochastic analysis of engineering systems, the task of generating samples according to a target probability distribution involving some performance function of the system response often arises. This paper introduces an adaptive method for rejection sampling that uses adaptive kernel sampling densities (AKSD) as proposal densities for the rejection sampling algorithm in an iterative approach. The AKSD formulation relies on having available (1) a small number of samples from the target density, as well as (2) evaluations of the system performance function over some other sample set. This information is then used to establish the adaptive features of the stochastic sampling involving (1) an explicit optimization of the kernel characteristics for reduction of the computational burden, and so maximizing sampling efficiency, and (2) selection of the exact model parameters to target so that potential problems when forming proposal densities for high-dimensional vectors are avoided. Beyond this theoretical formulation of the adaptive stochastic sampling, its implementation within the context of Subset Simulation (SS) is also demonstrated, with the AKSD method utilized for generating independent conditional failure samples. Additionally, a modified rejection sampling algorithm is proposed for using AKSD in SS that can significantly reduce the required number of simulations of the system model response.

Topics:
Density , Simulation , Approximation , Sampling methods
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000881.
Abstract 

Abstract  Axle loading spectrum inputs obtained from existing weigh-in-motion (WIM) stations are one of the key data elements required in the pavement mechanistic-empirical (ME) design. Because of limited number of WIM stations within a state agency, it is critical to implement clustering approaches to identifying similar traffic patterns and developing cluster average Level 2 inputs for a particular pavement design. Even though several states have applied clustering methods for this purpose, they rely solely on hierarchical-based method. Many other types of clustering techniques based on different induction principles are available but have not been tested. In this paper, four types of clustering methods, including agglomerative hierarchical, partitional K-means, model-based, and fuzzy c-means algorithms, are implemented to cluster traffic attributes for pavement ME design using data sets from 39 WIM sites in Michigan. Two case studies, one flexible pavement and one rigid pavement, are conducted. The impacts of using various clustering methods for preparation of Level 2 traffic inputs on pavement performance are examined. Cosine similarity analyses reveal that the four clustering methodologies generate highly comparable traffic inputs and predicted pavement performance as compared to the Level 1 results. However, the equivalent single axle loads (ESALs) from the four clustering methods can result in 12.7 mm (0.5 in.) of difference of designed surface layer thickness. The hierarchical method consistently has the lowest cosine similarity values, the fuzzy-based method has the highest similarity, while the other two clustering methods generally outperform the hierarchical method if Level 1 site-specific results are set as the benchmark. This study raises the awareness that more research is desired to select the most appropriate clustering approach for the development of Level 2 traffic inputs based on existing WIM data sets for pavement ME design.

Topics:
Design , Traffic , Trucks , Pavement
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000889.
Abstract 

Abstract  Data mining is a discovery procedure to explore and visualize useful but less-than-obvious information or patterns in large collections of data. Given the amount and varying parameter types in a large data set such as that of the National Bridge Inventory (NBI), using traditional clustering techniques for discovery is impractical. As a consequence, the authors have applied a novel data discovery tool, called Two-step cluster analysis, to visualize associations between concrete bridge deck design parameters and bridge deck condition ratings. Two-step cluster analysis is a powerful knowledge discovery tool that can handle categorical and interval data simultaneously and is capable of reducing dimensions for large data sets. The analysis, of a total of 9,809 concrete highway bridge decks in the Northeast climatic region, found that bridges with cast-in-place decks that have a bituminous wearing surface, a preformed fabric membrane, and epoxy-coated reinforcement protection are strongly associated with the high condition ratings for bridge decks regardless of the average daily truck traffic (ADTT). Conversely, results show that bridges with cast-in-place bridge decks that have a bituminous wearing surface but have neither a deck membrane nor deck reinforcement protection are strongly associated with low condition ratings for bridge decks regardless of the ADTT. It was concluded that Two-step cluster analysis is a useful tool for bridge owners and agencies to visualize general trends in their concrete bridge deck condition data and to support them in their decision-making processes to effectively allocate limited funds for maintenance, repair, and design of bridge decks.

Topics:
Bridges (Structures) , Concretes , Data mining
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2015;3(2):. doi:10.1061/AJRUA6.0000859.
Abstract 

Abstract  As traditionally infrastructure-centric industries such as the railways deploy ever more complex information systems, data interoperability becomes a challenge that must be overcome in order to facilitate effective decision making and management. In this paper, the authors propose a system based on semantic data modeling techniques to allow integration of information from diverse and heterogeneous sources. The results of work, which aimed to demonstrate how semantic data models can be used in the rail industry, are presented. These include a novel domain ontology for the railways, and a proof-of-concept real time passenger information system based on semantic web technologies. Methods and patterns for creating such ontologies and real world systems are discussed, and ontology-based techniques for integrating data with legacy information systems are shown.

Topics:
Ontologies , Data fusion , Rails
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2016;3(2):. doi:10.1061/AJRUA6.0000868.
Abstract 

Abstract  A maintenance problem can be regarded as an optimization task, where the solution is a trade-off between the costs associated with inspection and repair activities and the benefits related to the faultless operation of the infrastructure. The optimization aims at minimizing the total cost while tuning some parameters, such as the number, time, and quality of inspections. Due to the unavoidable uncertainties, the expected cost of maintenance and failure can only be estimated by assessing the reliability of the system. The problem is, therefore, formulated as a time-variant reliability-based optimization, where both objective and constraint functions require the assessment of reliability with time. This paper proposes an efficient general numerical technique to solve this problem by means of just one single reliability analysis, while explicitly taking the diverse forms of uncertainty into account. The technique is generally applicable to any problem where the ageing or damage propagation process is known by means of input-output relationships, which apply to a great number of the cases. This technique exploits a Monte Carlo strategy derived from the concept of forced simulation, which significantly increases the efficiency of computing the optimal solution. The efficiency and accuracy of the proposed approach is shown by means of an example involving a fatigue-prone weld in a bridge girder.

Topics:
Inspection , Maintenance , Simulation , Design

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