Public infrastructure is not merely a portfolio of physical assets; it is a network architecture. Bridges serve as nodes in transportation graphs; dams anchor water-energy nexuses; tunnels function as choke points whose disruption cascades into urban congestion and economic loss. This interdependence generates a risk profile that defies asset-by-asset analysis: identical local damage can yield vastly different systemic consequences depending on network topology, redundancy, and real-time demand~\cite{frangopolBridgeLifecyclePerformance2017, salmanStrategicFrameworkUrban2025, yeterReviewArtificialIntelligenceaided2022}. Consider the energy sector. Dams provide hydroelectric generation, transmission-supporting bridges carry critical power corridors, and transport networks supply fuel to thermal plants. Structural degradation at any node threatens energy continuity and regional economic stability~\cite{lainoCoastalHazardsComprehensive2024}. The scale of exposure is not trivial: a 2019 ICOLD survey documented over 58,000 large dams worldwide that are approaching or have exceeded their design life, with direct implications for approximately 16\% of global electricity generation capacity~\cite{icold2020wrD}. Deterioration mechanisms compound this urgency. Steel components accumulate fatigue damage under millions of traffic induced stress cycles~\cite{alsitExperimentalNumericalInvestigation2024}. Reinforced concrete suffers chloride ingress and rebar corrosion, often invisible until spalling reveals advanced decay~\cite{harbulakovaUsingMathematicalNumerical2017}. Foundations in river and coastal environments contend with scour and differential settlement~\cite{hamzehPerformanceAssessmentSubmerged2025}. Dams experience evolving seepage paths and hydro-mechanical loading; stadiums and large public buildings undergo complex interactions among occupancy loads, vibration, seismic effects, and material aging. Here lies a fundamental problem: conventional inspection regimes periodic, largely visual miss the progression of these mechanisms. Symptoms are intermittent, localized, or concealed beneath protective layers. The relationship between observable indicators and remaining service life is rarely linear. Consequently, engineers make high stakes decisions on insufficient evidence, oscillating between excessive conservatism and delayed action that compounds risk~\cite{frangopolBridgeLifecyclePerformance2017, gengIntegratingMultisourceData2025}. Multi-sensor detection and data fusion offer a pathway to bridge this information gap, linking field measurements to damage-sensitive features and tracking degradation evolution over time. But monitoring data become actionable only when coupled with mechanistic understanding and explicit uncertainty quantification. Raw signals and isolated threshold alerts are not decision support; they are noise with infrastructure implications~\cite{patelPhysicstrainedArtificialIntelligence2025, xuBigModelStrategy2024}. The organizational reality is often grimmer than the technical possibility. Despite decades of progress, many agencies operate on fragmented data ecosystems and reactive maintenance paradigms. Asset information—scattered across inspection notes, sensor logs, and archival records rarely feeds into coherent longitudinal analysis~\cite{jaberiEvaluationDigitalCapabilities2025}. Digital tools, including much discussed digital twins, have not fully closed this gap. Data acquisition has been prioritized over decision integration; implementations stumble over inconsistent standards, limited validation, and unclear governance. The result is a proliferation of methods without a principled framework for comparing them or justifying their adoption~\cite{zhangDigitalTwinDrivenIntelligent2021, salmanStrategicFrameworkUrban2025}.

Think of public infrastructure less like a bunch of separate buildings and more like one big connected web. Bridges work like connection points in our transportation system, dams are the heart of our water and energy setup, and tunnels are bottlenecks that can mess up entire cities if something goes wrong. Because everything's so connected, you can't just look at each piece by itself - the same damage might barely matter in one spot but completely wreck the system somewhere else, depending on how things are laid out and what backup options exist. Take our power grid, for example. Dams generate electricity, bridges carry power lines across rivers, and roads bring fuel to power plants. If any one of these breaks down, it can knock out power and hurt the local economy. And we're talking about serious numbers here - a 2019 survey found over 58,000 big dams around the world that are getting old or already past their expected lifespan, and these supply about 16% of all the electricity we use globally. What makes this even scarier is how these structures actually break down. Steel parts get worn out from millions of cars and trucks driving over them. Concrete gets eaten away by salt and water, and by the time you can see the damage on the surface, it's often pretty bad underneath. Foundations near water get washed out or sink unevenly. Dams develop new leaks and deal with changing water pressures, while big buildings like stadiums have to handle crowds, vibrations, earthquakes, and just plain getting older. Here's the real kicker: the way we usually check on these things - sending someone out every so often to take a look - completely misses how this damage actually happens. Problems come and go, show up in weird places, or stay hidden under protective coatings. And there's rarely a straight line between what you can see and how much life is left in the structure. So engineers end up making huge decisions without enough information, either being way too cautious or waiting too long and making things worse. Using multiple sensors and combining their data could help fill in these information gaps by connecting what we measure to actual damage and tracking how things get worse over time. But all that monitoring data only helps if we really understand what's happening and can account for what we don't know. Just having raw numbers and basic alarms isn't actually helpful for making decisions - it's just more noise that infrastructure managers have to deal with. The real-world situation is often worse than what's technically possible. Even after decades of improvements, many agencies still work with scattered information and just fix things when they break. All the data from inspections, sensors, and old records rarely gets put together into any kind of useful long-term picture. Digital tools, including those digital twins everyone talks about, haven't really solved this problem. We've focused more on collecting data than on using it to make better decisions, and projects keep running into problems with inconsistent standards, limited testing, and unclear rules about who's responsible for what. The end result is tons of different methods but no real way to compare them or figure out which ones are actually worth using.