PSI - Issue 84

Giuseppe Alessandro Battista et al. / Procedia Structural Integrity 84 (2026) 694–701

695

1. Introduction The safety and structural integrity of road and highway infrastructure currently represent one of the primary strategic challenges for national and European transport systems. In Italy, the critical nature of this issue has been dramatically underscored in recent years by several structural failures that highlighted the extreme vulnerability of an extensive, heterogeneous, and partially obsolete infrastructural heritage. Within this evolving context, the Italian legislator initiated a deep process of institutional and regulatory renewal, which culminated in the establishment of the National Agency for the Safety of Railways and Road and Highway Structures (ANSFISA). This Agency is tasked with critical oversight and policy-setting functions, aiming to standardize safety protocols and inspection regimes across a landscape of operators that remains historically fragmented and technically diverse. Alongside the Agency's supervisory role, the direct responsibility of road and highway Operators - both public and private - remains the central pillar of the system. These entities are legally and operationally responsible for ordinary and extraordinary maintenance, as well as the long-term planning of complex interventions on bridges, viaducts, tunnels, and pavements. This creates a multi-layered governance system where public oversight requirements must coexist with intricate management planning logics. Both levels are increasingly constrained by severe resource limitations, technical complexities, and rising social expectations regarding infrastructure resilience and service continuity. In this scenario, the transition toward sophisticated data-driven decision-making is no longer an option but a necessity to support sustainable asset management and ensure continued urban mobility (Alnaqbi et al., 2025). To further streamline these processes, conceptual advancements such as Smart Contracts are being explored to automate maintenance workflows, thereby reducing administrative overhead and enhancing data transparency (Villa et al., 2025). Furthermore, the integration of Digital Twin technology and IoT platforms offers high-fidelity virtual representations updated with real-time data, which are crucial for both routine monitoring and rapid disaster response (Kaveh and Alhajj, 2025). A crucial node in this complex system concerns the objective prioritization of interventions and audits. Defining which infrastructures to inspect or rehabilitate, and in what specific order, represents a strategic choice with direct implications for public safety and economic sustainability. Recent empirical studies have demonstrated that pavement condition, often quantified through the International Roughness Index (IRI), plays a decisive role in accident frequency and the overall safety profile of rural and urban roads (Ghanbari et al., 2019). Beyond the visible road surface, the long-term behavior of earthwork assets, such as geotechnical slopes, must be meticulously incorporated into deterioration models to prevent catastrophic slope failures from compromising network safety (Marin and Long, 2024). Modern management frameworks now include risk-based prioritization systems that rank intervention scenarios by integrating probabilistic structural reliability models with predictive maintenance data (Brighenti et al., 2025). This granular focus is particularly critical for bridge networks, where unforeseen failure modes can arise even in properly designed structures due to subtle errors in management or inspection gaps (Pucci et al., 2020). To effectively manage these complexities at a network-wide level, infrastructure asset management must handle multiple sources of stochastic uncertainty, including material degradation and extreme weather events. Stochastic based performance models are increasingly favored to capture the non-linear and time-dependent deterioration mechanisms that traditional deterministic models often fail to predict accurately (Shahid et al., 2025). Additionally, the ultimate effectiveness of these predictive models relies heavily on sophisticated data pre-processing strategies and the systematic benchmarking of various forecasting techniques to ensure that the chosen algorithms provide reliable and actionable results (Wang et al., 2025). From an economic perspective, effective resource allocation requires a rigorous Life Cycle Cost (LCC) approach, ensuring that maintenance strategies are not only technically sound but also economically optimized over the entire lifecycle of the asset (Oh et al., 2023). Advanced mathematical tools, such as mixed-integer programming and multi-agent reinforcement learning, are further employed to maximize overall network conditions even under the pressure of high budget volatility (Asghari et al., 2025). Technological innovation is further driving operational efficiency through the deployment of Unmanned Aerial Vehicles (UAVs) and computer vision for the autonomous monitoring of urban infrastructure (O’Shaughnessy et al., 2024). These aerial tools support advanced decision support frameworks (DSS) specifically tailored for specialized needs, such as the localized remediation of timber bridge structures (Rashidi et al., 2025, 2020). In the urban context, novel methodologies are emerging that combine historical accident data with proactive risk assessment and cost benefit analysis to prioritize high-impact safety measures, such as the redesign of critical intersections (Toraldo et al.,