The effective functioning of modern society relies on the continuous services provided by infrastructure systems, such as transportation networks, power grids, and water supply systems. These systems are growing in complexity with respect to their spatial scales, interdependencies, and a variety of stakeholders involved throughout their extensive life cycles. Simultaneously, infrastructure operating environments are undergoing substantial changes driven by ageing assets, budget constraints, rising service demands, and climate-induced stressors.

Faced with these challenges, conventional approaches based primarily on manual, static decision-making for infrastructure planning and management have become increasingly inadequate. Instead, infrastructure sectors require a paradigm shift towards a more proactive, data-driven decision-making methodologies. Such methodologies can capture the evolving operational conditions, predict the resulting effects on infrastructure performance, and effectively engage diverse stakeholders, including infrastructure owners, operators, maintenance providers, and end users, into decision-making processes.

To facilitate this transition, advancements in onboard monitoring, sensor fusion, and predictive analytics can be leveraged to support early detection of system deterioration and timely anticipation of service disruptions, which then informs optimal resource allocation while ensuring that infrastructure systems meet minimum performance requirements consistently.