PSI - Issue 84

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

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Figure 3: Comparison of intervention prioritization methods

7. Remarks This paper presented a comparative analysis of two intervention prioritization methodologies developed by two Italian bridge operators, SINA and APT, applied to the same bridge network in order to explicitly assess their implications on decision-making outcomes. The comparison highlighted that the two approaches can lead to significantly different prioritization results. Both methodologies are grounded in a risk-based perspective and explicitly consider structural capacity, demand, and consequences. They can be formulated to account for multiple hazard sources (e.g., seismic or hydraulic) and are flexible with respect to the limit states included in the analysis, depending on data availability and decision-making objectives. Differences between the two approaches arise from the prioritization metric adopted, deferral cost or cost effectiveness of the interventions, and on how consequences and failure probabilities are modeled rather than from the types of risks or performance aspects considered. The first approach, based on the cost associated with postponing interventions, emphasizes network-level impacts and socio-economic consequences of service disruption. In the analyzed bridge sample, this resulted in higher priorities for motorway bridges, that are characterized by high costs of disruption and human losses in case of collapse. While explicitly including uncertainty in action effects and resistance, the approach does not account for the time dependent evolution of defects and relies on simplified assumptions for degradation and failure probability estimation. The second approach, implemented within the APT BMS, adopts a risk-driven framework that links structural reliability, defect presence and evolution, consequences, and intervention costs through a Priority Index. The method shows great sensitivity to the actual state of conservation of the assets and to defect-driven vulnerability and, in subsequent studies, it has been further developed to explicitly account for the degradation of defects over time [18]. The definition of damage levels and defect-related parameters, however, is not uniquely standardized and may introduce subjectivity, while uncertainty is not explicitly modeled, as the method was originally conceived to be usable even in the absence of in-depth (FEM) structural analyses. The comparison confirms that different operational objectives and decision-making contexts lead to diverse prioritization results. However, the applications of the two methods may be complementary if the APT method is used as a filter to apply before implementing a detailed analysis on the bridges with higher priority Index and finally prioritizing the interventions based on the SINA approach. From a broader perspective, the results underline the importance of Decision Support Systems (DSS) capable of transparently integrating probabilistic assessments, consequences, and economic considerations, moving beyond purely condition-based strategies. Ultimately, the study emphasizes that prioritization outcomes are inherently dependent on the underlying assumptions, data requirements, and modelling choices embedded within each methodology. Explicitly comparing different DSS-based approaches, as proposed in this work, represents a valuable step toward more informed, traceable, and context-aware decision-making in bridge management.