Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Goforth, Eric; El‐Dakhakhni, Wael; Wiebe, Lydell

doi:10.1061/(asce)is.1943-555x.0000667

Cited by 5 publications

(7 citation statements)

References 52 publications

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“…A better understanding of a project's complexity can reduce its vulnerability to complexity-induced risks (Zhang 2007;Gao et al 2018) especially because the latter are more relevant to megaprojects (Locatelli et al 2017). Systemic risks, which pertain to the probability of cascading failures that may lead to a complete systemlevel collapse, are consequences of such complexity (Liu et al 2019b;Eisenberg et al 2020;Goforth et al 2022). These risks are typically triggered by the complex dynamic interdependence between different project stakeholders.…”

Section: Infrastructure Project Complexity and Systemic Risk Analysismentioning

confidence: 99%

“…For example, it has been quantified based on the stakeholders' involvement in the activities network (Zhu and Mostafavi 2017) or based on their resource sharing or contractual relationships (Mok and Shen 2016;On Cheung et al 2018). Other studies have quantified interdependence based on the knowledge transfer among project stakeholders (Chinowsky et al 2011;Garcia et al 2021;Goforth et al 2022). In the study performed by Qiang et al (2021), interdependence was quantified using a dynamic network approach, considering the number of collaborations between different stakeholders.…”

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

“…More recently, Gondia et al ( 2022) quantified contractors' interdependence based on their number of mutual workdays at different project periods by means of a dynamic network. Gondia et al (2022) reasoned that contactors' interdependence is a consequence of project tasks' complexity where contractors from different disciplines work in parallel (i.e., timewise) within the same vicinity (i.e., spacewise) to deliver the project scope. Their study proposed utilizing historical data correlation analyses between project KPIs and interdependence network characteristics to evaluate contractors' interdependence-induced risks on project performance.…”

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

“…Fig. 1 illustrates the framework proposed by Gondia et al (2022), which introduced a radical approach to managing systemic risks resulting from contractors' interdependence.…”

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

“…Such an issue may cause cascade disruptions within other months if only the interdependence risks within a single month are managed without considering the entire set of project dynamic networks. In other words, improvement of the project performance at one time period (e.g., month) may lead to the degradation of the whole project's performance throughout its timeline-an issue that has not been addressed by Gondia et al (2022). In addition, Gondia et al (2022) did not present the formulation and explanation pertaining to optimizing the project's networks.…”

Section: Current Limitations In Project Risk Management Practicesmentioning

confidence: 99%

See 4 more Smart Citations

Managing Interdependence-Induced Systemic Risks in Infrastructure Projects

Moussa

El‐Dakhakhni

2022

J. Manage. Eng.

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Complex by nature, infrastructure megaprojects rarely meet stakeholders' expectations. A key characteristic of such complexity is the interdependence among different project stakeholders (e.g., contractors) where disruption of one contractor's work may instigate (systemlevel) systemic risks, resulting in poor key performance indicators (KPIs) of the whole project. Attributed to the lack of appropriate analysis and quantification tools, managing the systemic risks resulting from such interdependence remains challenging. The current study fills this knowledge gap through proactive systemic risk management (i.e., early identification, analysis, mitigation, and continuous monitoring).The study enhanced and operationalized a previously developed conceptual framework, in which interdependence was quantified through employing complex network theoretic measures. Specifically, the current study correlated contractor interdependence to project KPIs in order to assess associated project systemic risks. Subsequently, a metaheuristic optimization technique was employed to reorganize the project's schedule-effectively managing interdependence-induced risks. To demonstrate the utility of the developed methodology, a power infrastructure project was considered. Finally, the study provides valuable insights to improve the performance of complex infrastructure projects through proactive systemic risk management, ultimately enhancing the overall project's hyper resilience (i.e., to interdependence-induced disruptions).

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Section: Infrastructure Project Complexity and Systemic Risk Analysismentioning

confidence: 99%

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

“…Fig. 1 illustrates the framework proposed by Gondia et al (2022), which introduced a radical approach to managing systemic risks resulting from contractors' interdependence.…”

Section: Systemic Risk Management and Project Key Performance Indicatorsmentioning

confidence: 99%

Section: Current Limitations In Project Risk Management Practicesmentioning

confidence: 99%

See 3 more Smart Citations

Managing Interdependence-Induced Systemic Risks in Infrastructure Projects

Moussa

El‐Dakhakhni

2022

J. Manage. Eng.

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Infrastructure Asset Management System Optimized Configuration: A Genetic Algorithm–Complex Network Theoretic Metamanagement Approach

Goforth

Yosri

El‐Dakhakhni

et al. 2022

J. Infrastruct. Syst.

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An effective infrastructure asset management (AM) system is crucial for utilities, city officers, government agencies, and other asset-owning organizations to facilitate navigating the numerous challenges associated with operating and managing infrastructure assets. In this paper, the AM system itself is represented as a complex network (comprised of nodes and links) that describes the major components necessary for its operation within an organization and the information connections between such components. ISO 55001, a widely accepted international standard, specifies the requirements for an effective AM system and outlines the criticality levels of different system components-reflected in the corresponding network by the link weights. The main challenges facing managing an AM system (i.e., metamanagement) pertain to (1) information asymmetry (i.e., not relying on consistent information for decision making) between AM system components; and (2) information overload (i.e., excessive information undermining decision making) within the AM system components. These challenges cause systemic risks (possibility of dependence-induced disruptions) within the system network due to the connectedness of system components. Systemic risks can be mitigated through built-in network resilience by restructuring the system component connections. Such network reconfiguration presents a complex nonconvex optimization problem with multiple potential solutions depending on the number of new connections to be added to the AM system, the length of those connections, and the target risk mitigation level. Through this metamanagement (managing the management system) lens, a genetic algorithm approach was employed to explore the optimal AM network configurations considering different objective functions. These objective functions were based on different complex network measures including the betweenness-, closeness-, and eigenvector-centrality, as well as the vulnerability index. The devised objective functions were employed to the cases of adding 1 to 15 links only to limit network overconnectedness (i.e., information overload). Considering all objective functions evaluated, adding a small number of links (fewer than five) provided a significant reduction in systemic risk (18% to 49%). Finally, managerial insights are presented to explain how to employ the developed approach to mitigate the systemic risks within an organization's AM system based on different metrics valuations and stakeholder inputs.

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