PSAM 16 - Session T25 Overview

PSAM 16 Conference Session T25 Overview

Session Chair: Yong-Joon Choi (yong-joon.choi@inl.gov)

Paper 1 AD111

Lead Author: Austin Lewis Co-author(s): Katrina M. Groth (kgroth@umd.edu)

Impact of Complex Engineering System Data Stream Discretization Techniques on the Performance of Dynamic Bayesian Network-Based Health Assessments
Critical infrastructure in the energy and industry sectors is dependent on the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to be able to monitor their system health and make informed decisions on maintenance and risk management practices. One proposed approach is the use of a causal-based model such as a Dynamic Bayesian Network (DBN) that contains the structural logic of and provides a graphical representation of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing the underlying conditional probability tables (CPTs) impact the DBN's system health estimates. Using a range of metrics designed for comparing health management models, this paper demonstrates the impact that different time discretization strategies have on the performance of DBN models built for CES health assessments. Using simulated nuclear data of a sodium fast reactor (SFR) experiencing a transient overpower (TOP), different strategies for discretizing CES data streams are used to construct the CPTs for a health-based DBN model. This leads to different models determining different assessments of overall system health. By understanding how these design factors impact the model’s health assessments, future risk models can be developed to provide a more meaningful assessment of a system’s health, resulting in more informed decisions.

Impact of Complex Engineering System Data Stream Discretization Techniques on the Performance of Dynamic Bayesian Network-Based Health Assessments

Critical infrastructure in the energy and industry sectors is dependent on the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to be able to monitor their system health and make informed decisions on maintenance and risk management practices. One proposed approach is the use of a causal-based model such as a Dynamic Bayesian Network (DBN) that contains the structural logic of and provides a graphical representation of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing the underlying conditional probability tables (CPTs) impact the DBN's system health estimates. Using a range of metrics designed for comparing health management models, this paper demonstrates the impact that different time discretization strategies have on the performance of DBN models built for CES health assessments. Using simulated nuclear data of a sodium fast reactor (SFR) experiencing a transient overpower (TOP), different strategies for discretizing CES data streams are used to construct the CPTs for a health-based DBN model. This leads to different models determining different assessments of overall system health. By understanding how these design factors impact the model’s health assessments, future risk models can be developed to provide a more meaningful assessment of a system’s health, resulting in more informed decisions.

Paper AD111 | Download the paper file. | Download the presentation PowerPoint file.

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Paper 2 JO134

Lead Author: Josefin Lindström Co-author(s): Jonas Johansson, jonas.johansson@risk.lth.se

Towards Conceptualizing and Modelling Critical Flows
Continuous access to flows of goods and services, such as energy, transport, information, and food, is essential and the basis for functioning modern societies. Hence, they are critical and need to be secured, as highlighted in USA Presidential Policy Directive (PPD-21) and EU Directives (EPCIP, NIS). Mega-trends such as climate change, changing geopolitical environment, hybrid- and cyber threats, globalization of supply chains, and rapid technological advances come with new challenges and emerging threats to securing flows. Given the complexities involved in securing critical flows, the focus should not only be on protection but also resilience. Critical flows are fundamentally enabled and accommodated by critical infrastructures and supply chains of diverse natures. Further, the interconnectedness of infrastructures and leanness of supply chains lead to an increased vulnerability where no single entity has a comprehensive overview and understanding over their interconnected form and function, i.e., their joint structure, connectivity, flow volumes, and spatial and temporal distributions. We argue that there is an added value to the critical infrastructure, supply chain, and security of supply regimes to establish a common flow concept to more holistically study the protection and resilience of societal functionality. Critical flows can take vastly different forms. They can consist of different entities, such as people, goods, energy, or data; they can span different geographical scales, from locally to globally; they can require an array of different physical infrastructures and supply chains. Critical flows are hence of heterogeneous nature, presenting difficulty in establishing a common flow concept and enabling holistic modelling and simulation. We argue that a more aggregated level of abstraction, i.e., flows, will aid as a useful and complementing perspective when studying interdependencies and interconnectedness between physical sectors, infrastructures and supply chains. The aim of the paper is to conceptualize and outline an initial modelling and simulation approach to grasp the nature and behavior of flows and their interdependencies. In this paper, the main contributions are: 1) a conceptualization of critical flows applicable for the understanding and analysis of a wide variety of vital societal flows, focusing on salient properties, 2) the outlining of a generic model for simulation and analysis of interconnected flows, and 3) an illustration of the approach in a Swedish setting with respect to the flows of food, transportation, and energy, by analyzing their interdependent behavior and vulnerabilities. This work is part of a larger research project with the aspiration to contribute to the security and resilience of flows through an enhanced understanding of their interdependencies. Initial conclusions are that the concept of critical flows beneficially integrates and complements existing perspectives and where modelling and simulation approaches that enable this generic perspective are required towards assessing and managing vital societal challenges. Keywords: Critical Flows, Critical Infrastructure, Supply Chain, Modelling, Interdependencies, Security, Resilience Authors and affiliations: Josefin Lindström, Division of Risk Management and Societal Safety, Lund University, Sweden; and Jonas Johansson, Division of Risk Management and Societal Safety, Lund University, Sweden

Towards Conceptualizing and Modelling Critical Flows

Continuous access to flows of goods and services, such as energy, transport, information, and food, is essential and the basis for functioning modern societies. Hence, they are critical and need to be secured, as highlighted in USA Presidential Policy Directive (PPD-21) and EU Directives (EPCIP, NIS). Mega-trends such as climate change, changing geopolitical environment, hybrid- and cyber threats, globalization of supply chains, and rapid technological advances come with new challenges and emerging threats to securing flows. Given the complexities involved in securing critical flows, the focus should not only be on protection but also resilience. Critical flows are fundamentally enabled and accommodated by critical infrastructures and supply chains of diverse natures. Further, the interconnectedness of infrastructures and leanness of supply chains lead to an increased vulnerability where no single entity has a comprehensive overview and understanding over their interconnected form and function, i.e., their joint structure, connectivity, flow volumes, and spatial and temporal distributions. We argue that there is an added value to the critical infrastructure, supply chain, and security of supply regimes to establish a common flow concept to more holistically study the protection and resilience of societal functionality. Critical flows can take vastly different forms. They can consist of different entities, such as people, goods, energy, or data; they can span different geographical scales, from locally to globally; they can require an array of different physical infrastructures and supply chains. Critical flows are hence of heterogeneous nature, presenting difficulty in establishing a common flow concept and enabling holistic modelling and simulation. We argue that a more aggregated level of abstraction, i.e., flows, will aid as a useful and complementing perspective when studying interdependencies and interconnectedness between physical sectors, infrastructures and supply chains. The aim of the paper is to conceptualize and outline an initial modelling and simulation approach to grasp the nature and behavior of flows and their interdependencies. In this paper, the main contributions are: 1) a conceptualization of critical flows applicable for the understanding and analysis of a wide variety of vital societal flows, focusing on salient properties, 2) the outlining of a generic model for simulation and analysis of interconnected flows, and 3) an illustration of the approach in a Swedish setting with respect to the flows of food, transportation, and energy, by analyzing their interdependent behavior and vulnerabilities. This work is part of a larger research project with the aspiration to contribute to the security and resilience of flows through an enhanced understanding of their interdependencies. Initial conclusions are that the concept of critical flows beneficially integrates and complements existing perspectives and where modelling and simulation approaches that enable this generic perspective are required towards assessing and managing vital societal challenges. Keywords: Critical Flows, Critical Infrastructure, Supply Chain, Modelling, Interdependencies, Security, Resilience Authors and affiliations: Josefin Lindström, Division of Risk Management and Societal Safety, Lund University, Sweden; and Jonas Johansson, Division of Risk Management and Societal Safety, Lund University, Sweden

Paper JO134 | Download the paper file. |

Name: Josefin Lindström (josefin.lindstrom@risk.lth.se)

Bio: Josefin is a PhD student in the division of Risk Management and Societal Safety at Lund University's faculty of engineering in Sweden. She holds a BSc. degree in environmental engineering and an MSc. degree in risk management & safety engineering. Her research is on the interdependencies and vulnerabilities of critical societal flows and infrastructures.

Country: SWE
Company: Lund University
Job Title: PhD Student

Paper 3 JI6

Lead Author: Yail Kim Co-author(s): Raymon Nickle

Reliability of CFRP-Prestressed Concrete Girders for Highway Bridges
This research discusses the reliability of concrete bridge girders prestressed with carbon fiber reinforced polymer (CFRP) composite tendons. Of interest is the calibration of strength reduction factors to accommodate the safe operation of such bridge structures. Benchmark superstructures are design in accordance with the American Association of State Highway Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) Bridge Design Specifications (BDS) and the American Concrete Institute Committee 440 document. Uncertainty is taken into consideration through stochastic modeling, which is validated against published data. Design proposals are provided for practitioners to implement.

Reliability of CFRP-Prestressed Concrete Girders for Highway Bridges

This research discusses the reliability of concrete bridge girders prestressed with carbon fiber reinforced polymer (CFRP) composite tendons. Of interest is the calibration of strength reduction factors to accommodate the safe operation of such bridge structures. Benchmark superstructures are design in accordance with the American Association of State Highway Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) Bridge Design Specifications (BDS) and the American Concrete Institute Committee 440 document. Uncertainty is taken into consideration through stochastic modeling, which is validated against published data. Design proposals are provided for practitioners to implement.

Paper JI6 | Download the paper file. |

Name: Yail Kim (jimmy.kim@ucdenver.edu)

Bio: Dr. Jimmy Kim is President of the Bridge Engineering Institute, An International Technical Society, and a Professor in the Department of Civil Engineering at the University of Colorado Denver, Colorado, USA. His research interests encompass advanced composite materials for rehabilitation, structural informatics, complex systems, and science-based structural engineering, including statistical, interfacial, and quantum physics.

Country: USA
Company: University of Colorado Denver
Job Title: Professor

Paper 4 TA160

Lead Author: Shunichi Tada Co-author(s): Taro Kanno, kanno@sys.t.u-tokyo.ac.jp

Applying the Genetic Algorithm for Finding the Worst Scenario for the Post-Disaster Recovery of Water Distribution Network
Because water is an essential resource for numerous activities, a water distribution network (WDN) is one of the most important lifelines; therefore, considerations must be made to prepare for the restoration of WDNs during post-disaster periods. To evaluate the restoration plan of damaged pipes, we are developing the agent-based simulation that can reproduce the restoration processes of the WDNs and how the restoration plan affects to the performance of each subsystem of a city during the post-disaster periods. In many researches, the damage scenario was usually manually generated; the number of damaged pipes was estimated by an empirical equation considering the magnitude of earthquake and the properties of pipes, while a geographical distribution of the damaged pipes was randomly selected. In our previous research, it was found that the performance of the restoration plan is highly dependent on the geographical distribution of the damaged pipes. Therefore, it is difficult to appropriately evaluate the resilience of WDNs using such randomly generated scenarios, rather it is necessary to find the most difficult scenario under the given number of damaged pipes. In addition, scenarios for training and exercise should be designed and prepared by their difficulties for training objectives, not randomly or in an ad hoc manner. However, it was difficult to classify the scenarios according to their difficulties or characteristics since there are a huge number of possible damage distributions. In this research, we applied the genetic algorithm to explore the most difficult scenario of damage distribution to repair. In this GA, an individual represents one disaster scenario that describes a geographical distribution of the damaged pipes, and the population represents a set of various scenarios. As an objective function, we used a resilience triangle obtained from the simulation results. Through the evolutional operations, such as selection, crossover and mutation, the individual with the largest resilience triangle, the worst scenario, will be obtained. We can also obtain a set of scenarios with different resilience triangles, that is a set of scenarios ordered by their difficulties. We conducted a test search with one hundred damaged pipes out of more than 4000 pipes and confirmed our proposed method reaches to conversion after 100 generations. Analyzing the population after conversion, we found there are some common pipes in difficult scenarios, which suggests these pipes were critical for the WDNs resilience.

Applying the Genetic Algorithm for Finding the Worst Scenario for the Post-Disaster Recovery of Water Distribution Network

Because water is an essential resource for numerous activities, a water distribution network (WDN) is one of the most important lifelines; therefore, considerations must be made to prepare for the restoration of WDNs during post-disaster periods. To evaluate the restoration plan of damaged pipes, we are developing the agent-based simulation that can reproduce the restoration processes of the WDNs and how the restoration plan affects to the performance of each subsystem of a city during the post-disaster periods. In many researches, the damage scenario was usually manually generated; the number of damaged pipes was estimated by an empirical equation considering the magnitude of earthquake and the properties of pipes, while a geographical distribution of the damaged pipes was randomly selected. In our previous research, it was found that the performance of the restoration plan is highly dependent on the geographical distribution of the damaged pipes. Therefore, it is difficult to appropriately evaluate the resilience of WDNs using such randomly generated scenarios, rather it is necessary to find the most difficult scenario under the given number of damaged pipes. In addition, scenarios for training and exercise should be designed and prepared by their difficulties for training objectives, not randomly or in an ad hoc manner. However, it was difficult to classify the scenarios according to their difficulties or characteristics since there are a huge number of possible damage distributions. In this research, we applied the genetic algorithm to explore the most difficult scenario of damage distribution to repair. In this GA, an individual represents one disaster scenario that describes a geographical distribution of the damaged pipes, and the population represents a set of various scenarios. As an objective function, we used a resilience triangle obtained from the simulation results. Through the evolutional operations, such as selection, crossover and mutation, the individual with the largest resilience triangle, the worst scenario, will be obtained. We can also obtain a set of scenarios with different resilience triangles, that is a set of scenarios ordered by their difficulties. We conducted a test search with one hundred damaged pipes out of more than 4000 pipes and confirmed our proposed method reaches to conversion after 100 generations. Analyzing the population after conversion, we found there are some common pipes in difficult scenarios, which suggests these pipes were critical for the WDNs resilience.

Paper TA160 | Download the paper file. | Download the presentation PowerPoint file.

Name: Shunichi Tada (tada-shunichi513@g.ecc.u-tokyo.ac.jp)

Bio: Shunichi Tada is a Ph.D. student in the School of Engineering at the University of Tokyo. His research focuses on cognitive aspects of humans from the perspective of engineering and currently he is working on developing a system enhancing the interaction between human and computer-based technologies especialy the computational simulation.

Country: JPN
Company: School of Engineering, University of Tokyo
Job Title: Ph.D. student