PSI - Issue 84

Matteo Vozzi et al. / Procedia Structural Integrity 84 (2026) 425–432

426

1. Introduction The management of bridge networks is increasingly challenged by aging infrastructure, limited resources, and significant uncertainties affecting both structural performance and long-term degradation processes. In response to these challenges, the Italian bridge management landscape has recently undergone a significant transition following the introduction of the national Guidelines “Linee Guida per la classificazione e gestione del rischio, la valutazione della sicurezza ed il monitoraggio dei ponti esistenti” [1]. These guidelines marked a shift from predominantly qualitative, intervention-driven practices toward a structured, knowledge-based and risk-aware approach, promoting a progressive increase in knowledge of the bridge stock at network scale and postponing the definition of intervention programs until quantitative evaluations are available [2]. Despite this important step forward, the guidelines alone do not provide an operational framework for achieving an optimal and fully rational management of bridge stocks, both in Italy and internationally. A first limitation is related to the widespread use for existing structures of design-oriented standards, such as the Eurocodes, for the assessment of existing structures [3], which may not adequately reflect the actual condition and residual capacity of aging bridges, leading to conservative assessments and an overestimation of structural risk [4]. Further uncertainty arises from the difficulty of reliably evaluating the long-term effectiveness of maintenance and strengthening interventions, whose performance is often affected by durability-related knowledge gaps and limited long-term evidence [5]. As a result, existing degradation trajectories of bridges subjected to multiple interventions frequently deviate from established deterioration models, complicating long-term planning and prioritization [6]. To address these limitations, Bridge Management Systems (BMS) have progressively evolved toward more articulated Decision Support Systems (DSS), integrating inspection data, analytical models, and decision criteria to support transparent and traceable decision-making processes [7]. While DSS represent a significant advancement, they do not provide a unique or definitive solution to network-level prioritization problems. In practice, DSS frameworks differ in terms of modelling assumptions, data requirements, and decision criteria, and risk- and reliability-based methods are often employed primarily to support relative comparison among assets rather than the absolute quantification of collapse probability [8-11]. As a consequence, different DSS-based prioritization methodologies may lead to substantially different intervention rankings when applied to the same bridge network, each reflecting specific trade-offs between accuracy, robustness, data availability, and operational feasibility. Within this framework, the comparison of prioritization strategies adopted by different infrastructure operators becomes particularly valuable, as it allows to assess explicitly the practical implications of diverse methodological assumptions and input data on intervention ranking. The present paper contributes to this discussion by comparing two risk-based prioritization approaches currently employed by two Italian bridge operators: the methodology proposed by the SINA company, currently under experimental deployment, and that adopted by the Autonomous Province of Trento. Both approaches are applied to the same bridge network in Northern Italy, enabling a direct comparison of how structural assessments and maintenance interventions are prioritized and providing insight into the strengths, limitations, and operational suitability of each strategy. 2. SINA approach: prioritization method based on the postponement cost The first prioritization method, briefly summarized below, was developed by SINA, published in a series of papers, see [12–14], and applied on an experimental basis to a limited set of real highway bridges, with the aim of testing its effectiveness. The method is based on the estimation of the community cost that is associated with postponing an intervention to a bridge in the network. The method prioritizes interventions that are associated with the highest postponement costs. Let us consider a repair or strengthening intervention that has already been preliminarily scheduled for a bridge and let us assume that this intervention is postponed by a given time interval, for instance one year. The theoretical cost to society associated with postponing the execution of the intervention, , is expressed by Equation (1): =∑ ( − 0 ) (1)