Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Abstract

The ever-increasing investment gap for deteriorating infrastructure has necessitated the development of more effective asset management (AM) strategies. However, information asymmetry among AM stakeholder silos has been recognized as a key challenge in implementing effective AM strategies. The connectivity within the AM system introduces systemic risks (possibility of dependence-induced cascade failure) to the entire AM system operation when information asymmetry occurs. This study describes a toolbox to enable asset management stakeholders to assess such systemic risks through a network analytics approach. The network, representing the AM system, is examined through its centrality measures to identify the most critical subject areas within the AM system. These subject areas are subsequently paired with assets' key performance indicators (KPIs). Within the developed toolbox, descriptive analytics provide transferrable KPI insights between stakeholders to reduce key asset information asymmetry. In parallel, predictive analytics forecast KPIs, ensuring stakeholder awareness of future asset performance to allow for appropriate preparation. Subsequently, prescriptive analytics employ heuristic-based optimization for optimal configuration of the AM network. The five tools presented are as follows: (1) dependence identification and network modeling; (2) network centrality analysis; (3) descriptive analytics of critical subject area paired KPI; (4) KPI-based predictive analytics; and (5) prescriptive analytics for optimal network configuration. The utility of the developed toolbox is demonstrated for Tools 1–3 using a real AM system network and KPIs associated with power transmission infrastructure outages. Based on the analyses, managerial insights are drawn to illustrate the usefulness of the developed approach in improving information asymmetry within the AM system, subsequently mitigating dependence-induced systemic risks.