Public infrastructure is not a collection of physical assets; it should be viewed as a network of interdependent nodes with interconnected service networks. A bridge closure redirects traffic and delays emergency response; a dam failure disrupts water supply and hydroelectric power generation; a tunnel disruption triggers urban congestion. This interdependence gives pub- lic works a multifaceted risk profile stemming from isolated facilities: the same local damage can produce different consequences depending on the network’s role, redundancy, and demand patterns. From this perspective, smart monitoring becomes essential for resilience planning: critical informa- tion enables targeted interventions that maintain service while avoiding dis- ruptive closures, and robust evidence, quantified uncertainty, and traceable data are necessary to justify interventions and budgets. [3, 4, 5]. This interde- pendence is in energy-related infrastructures dams, transmission-supporting bridges, hydropower facilities, and transport corridors supplying energy sys- tems where structural degradation affects energy continuity and regional eco- nomic stability [6]. Scale of exposure isn’t banal: a 2019 ICOLD survey reported over 58,000 large dams worldwide approaching or exceeding their design life, with direct implications for approximately 16% of global elec- tricity generation capacity [7]. Mechanisms of deterioration aggravated this urgency. Steel components accumulate corrosion and fatigue under traffic- induced stress cycles [8]; reinforced suffers chloride ingress and rebar cor- rosion [9]; foundations are compromised by scour and settlement in river and coastal environments [10]. Dams and retaining structures face hydro- mechanical loading and evolving seepage paths; buildings and stadiums ex- perience complex interactions among occupancy, vibration, seismic effects, and material aging. What makes this particularly difficult is that conven- tional inspection regimes periodic, largely visual miss the progression of these mechanisms because symptoms are intermittent, localized, or concealed pro- tective layers. The relationship between observed indicators and remaining service life is rarely linear. This gives a deficiency of information: engineers make crucial decisions based on limited information, often oscillating between overly cautious interventions and late actions that increase risks. [3, 11]. Multimodal detection and data fusion offer a way for linking field measure- ments to damage-sensitive features and track the evolution of degradation 5 overtime. However, monitoring data are only actionable if associated with an understanding of mechanisms and explicit quantification of uncertain- ties; they aren’t actionable when providing raw signals or isolated threshold alerts. [12, 13]. Despite decades of progress, many agencies operate on frag- mented data and reactive maintenance asset information across inspection notes, sensor logs, and archives feeds into coherent longitudinal analysis [14]. Digital tools, including digital twins, haven’t fully closed this gap: data ac- quisition remains prioritized over decision integration, and implementation always runs into inconsistent standards, limited validation, and unclear gov- ernance. The result is a proliferation of methods without a framework for comparing them or justifying adoption. A constant review is needed, not to inventory the existing, but to differentiate capabilities from promising and unvalidated approaches, and to clarify areas where traditional engineering models remain indispensable.

Public infrastructure cannot be understood simply as a collection of individual physical structures. Instead, it functions as an interconnected network where each component depends on others through linked service systems. When a bridge closes, traffic gets rerouted and emergency services face delays. A dam failure affects both water supply and electricity generation. Tunnel problems create widespread urban traffic jams. These connections create complex risk patterns that extend far beyond individual facilities - the same type of local damage can have vastly different impacts based on how central that component is to the network, what backup options exist, and current demand levels. This interconnected nature makes intelligent monitoring crucial for building resilient infrastructure. Access to critical information allows for precise interventions that keep services running while preventing disruptive shutdowns. To justify these interventions and their costs, decision-makers need solid evidence, measured uncertainty levels, and traceable data [3, 4, 5]. These interdependencies become especially important in energy infrastructure - dams, bridges that support transmission lines, hydropower plants, and transportation routes that serve energy systems. When these structures deteriorate, they threaten both energy reliability and regional economic stability [6]. The scale of this challenge is significant. A 2019 ICOLD survey found more than 58,000 large dams worldwide that are nearing or have passed their intended lifespan, directly affecting roughly 16% of global electricity production [7]. Various deterioration processes make this situation more urgent. Steel parts develop corrosion and fatigue from repeated traffic loading [8]. Concrete structures suffer from chloride penetration and reinforcement bar corrosion [9]. Foundations get damaged by erosion and settling in river and coastal locations [10]. Dams and retaining walls face water pressure forces and changing seepage patterns. Buildings and stadiums deal with complicated interactions between occupancy loads, vibrations, earthquake effects, and material aging. The challenge becomes particularly difficult because traditional inspection methods - which happen periodically and rely mostly on visual assessment - fail to catch the progression of these deterioration processes. Symptoms often appear sporadically, stay confined to small areas, or remain hidden beneath protective coatings. The connection between visible signs and actual remaining useful life is typically not straightforward. This creates an information gap where engineers must make critical decisions with incomplete data, leading them to swing between unnecessarily cautious interventions and delayed actions that increase risk [3, 11]. Using multiple detection methods and combining different data sources provides a pathway for connecting field measurements to damage indicators and monitoring how deterioration progresses over time. However, monitoring data only becomes useful when paired with understanding of underlying mechanisms and clear uncertainty measurements. Raw signals or simple threshold warnings alone don't enable action [12, 13]. Even after decades of advancement, many agencies still work with disconnected data and reactive maintenance approaches - inspection reports, sensor records, and archived information don't get combined into comprehensive long-term analysis [14]. Digital technologies, including digital twins, haven't completely solved this problem. Data collection continues to take priority over decision-making integration, and implementation consistently encounters inconsistent standards, insufficient validation, and unclear management structures. This has led to a multiplication of methods without any framework for comparing their effectiveness or justifying their adoption. What's needed is ongoing evaluation - not simply cataloging what exists, but distinguishing proven capabilities from promising but unverified approaches, and identifying where traditional engineering models remain essential.