ANIDIS - L'ingegneria Sismica in Italia, ANIDIS XX - 2025

Dimensione del carattere:  Piccola  Media  Grande

Extension of segment-based risk classification methodology from road tunnels to bridges: a proposal for enhanced infrastructure assessment

Galileo Tamasi, Manuel Capogna, Emanuele Renzi, Maurizio De Angelis

Ultima modifica: 2025-08-01

Sommario


The Italian Ministry of Infrastructure and Transport has published two distinct sets of guidelines for the classification and risk management of existing infrastructures: one for bridges and another, more recent and structured, for road tunnels. The tunnel guidelines introduce a segment-based approach that divides the infrastructure into homogeneous units (called conci), allowing for the evaluation of partial and global attention classes—indirect indicators of risk. This methodology enables a detailed representation of the distribution of critical conditions along the tunnel. Conversely, the current methodology for bridges does not adopt this segmentation approach, leading to the assignment of a single attention class per structure—regardless of its complexity or length. As a result, a single-span bridge and a multi-span viaduct with varying environmental and structural conditions are described with the same level of granularity. This often results in an oversimplified average value that may obscure critical local vulnerabilities. The proposed methodology enables a more detailed assessment of attention classes across different structural segments, which is particularly useful for long viaducts with piers exposed to heterogeneous geohydrological risks, such as those partially immersed in riverbeds or located in areas prone to landslides. A practical evaluation is conducted through custom-programmed Excel spreadsheets simulating various scenarios. The study concludes with a proposal for a methodological revision of the current bridge guidelines, recommending the optional use of segment-based classification when infrastructure characteristics warrant a more granular risk analysis.


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