An official website of the United States government
Abstract Failures within water distribution systems are usually not isolated and tend to propagate to corresponding transportation infrastructure, yet most criticality and resilience analyses of water distribution networks are conducted for the individual water infrastructure without accounting for interdependence. To address this research gap, this study investigates how the critical components identified within water distribution systems may be different when accounting for failure propagation to the transportation road network. In this study, failure propagation is assumed to be based on geospatial interdependence and unidirectional, starting from water distribution network components to transportation network components. A logical interaction network is constructed considering the interdependence between both infrastructures, and multiobjective optimization is used to solve for the critical water distribution components considering: quantity of failures, performance loss, and financial costs. This work presents a modular workflow for water distribution criticality analysis and proposes the Kolmogorov‐Smirnov distance statistic between solution sets as a measure of the significance of interdependency for decision making. Results from the case study suggest that as the magnitude of water infrastructure failure increases beyond a threshold, the interdependency between water distribution and transportation becomes more significant. The difference between identified critical components using only information from water distribution and using both water distribution and transportation is significantly different (with greater than 95% confidence) for the city of Tampa, when more than 40 components fail (are isolated). These results will assist utilities in asset management and strategy assessment, by helping prioritize component repair and better allocate resources for critical interdependent infrastructures.
more »
« less
This content will become publicly available on August 1, 2025
Logical interdependencies in infrastructure: What are they, how to identify them, and what do they mean for infrastructure risk analysis?
Abstract A useful theoretical lens that has emerged for understanding urban resilience is the four basic types of interdependencies in critical infrastructures: the physical, geographic, cyber, and logical types. This paper is motivated by a conceptual and methodological limitation—althoughlogicalinterdependencies (where two infrastructures affect the state of each other via human decisions) are regarded as one of the basic types of interdependencies, the question of how to apply the notion and how to quantify logical relations remains under‐explored. To overcome this limitation, this study focuses on institutions (rules), for example, rules and planned tasks guiding human interactions with one another and infrastructure. Such rule‐mediated interactions, when linguistically expressed, have a syntactic form that can be translated into a network form. We provide a foundation to delineate these two forms to detect logical interdependence. Specifically, we propose an approach to quantify logical interdependence based on the idea that (1) there are certainnetwork motifsindicating logical relations, (2) such network motifs can be discerned from the network form of rules, and that (3) the higher the frequency of these motifs between two infrastructures, the greater the extent of logical interdependency. We develop a set of such motifs and illustrate their usage using an example. We conclude by suggesting a revision to the original definition of logical interdependence. This rule‐focused approach is relevant to understanding human error in risk analysis of socio‐technical systems, as human error can be seen as deviations from constraints that lead to accidents.
more »
« less
- Award ID(s):
- 1913920
- PAR ID:
- 10556822
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Risk Analysis
- ISSN:
- 0272-4332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract This paper proposes a novel simulation‐based hybrid approach coupled with time‐dependent Bayesian network analysis to model multi‐infrastructure vulnerability over time under physical, spatial, and informational uncertainties while considering cascading failures within and across infrastructure networks. Unlike existing studies that unrealistically assume that infrastructure managers have full knowledge of all the infrastructure systems, the proposed approach considers a realistic scenario where complete information about the infrastructure network topology or the supply–demand flow characteristics is not available while estimating multi‐infrastructure vulnerability. A novel heuristic algorithm is proposed to construct a dynamic fault tree to abstract the network topology of any infrastructure. In addition, to account for the unavailability of exact supply–demand flow characteristics, the proposed approach constructs the interdependence links across infrastructure network systems using different simulated parameters considering the physical, logical, and geographical dependencies. Finally, using parameters for geographical proximity, infrastructure managers' risk perception, and the relative importance of one infrastructure on another, the multi‐infrastructure vulnerability over time is estimated. Results from the numerical experiment show that for an opportunistic risk perception, the interdependencies attribute to redundancies, and with an increase in redundancy, the vulnerability decreases. On the other hand, from a conservative risk perspective, the interdependencies attribute to deficiencies/liabilities, and the vulnerability increases with an increase in the number of such interdependencies.more » « less
-
Abstract Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL withfactualEnglish, which is much less restrictive and requires very little training from users. However, KALMFLhas limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRAto enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRAachieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRAachieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRAby drawing attention to the problems faced by the recently made famous AI, ChatGPT.more » « less
-
Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development.more » « less
-
It is well known that interdependence between electric power systems and other infrastructures can impact energy reliability and resilience, but it is less clear which particular interactions have the most impact. There is a need for methods that can rank the relative importance of these interdependencies. This paper describes a new tool for measuring resilience and ranking interactions. This tool, known as Computing Resilience of Infrastructure Simulation Platform (CRISP), samples from historical utility data to avoid many of the assumptions required for simulation-based approaches to resilience quantification. This paper applies CRISP to rank the relative importance of four types of interdependence (natural gas supply, communication systems, nuclear generation recovery, and a generic restoration delay) in two test cases: the IEEE 39-bus test case and a 6394-bus model of the New England/New York power grid. The results confirm industry studies suggesting that a loss of the natural gas system is the most severe specific interdependence faced by this region.more » « less
