PSI - Issue 84

Mirko Calò et al. / Procedia Structural Integrity 84 (2026) 392–400

393

Nomenclature E p

Elastic modulus of uncorroded prestressing steel Mean compressive strength of concrete Conventional yielding strength of prestressing steel

f cm,b f p,01 f sy,b

Mean tensile strength of steel

Beam height

H b h bit

Road pavement thickness

Beam spacing

i b

Maximum span length Number of beams

L MAX

n b

Slab thickness

s

Amplification factor of Traffic Load Model 1 Ultimate strain of prestressing steel

α

ε pu ρ sp σ sp

Prestressing steel ratio

Residual stress level of prestressing steel

Equivalent percentage reduction of the corroded steel area

ξ

1. Introduction Bridge Authorities are nowadays involved in extensive maintenance activities of their infrastructures, as most of them are approaching the end of their expected service life. This occurrence has raised concerns about the capacity of existing bridges to withstand the combined effect of exogenous hazards such as natural, human-made (e.g., traffic and service loads) and atmospheric ones (e.g., material degradation and corrosion), while the number of structures belonging to bridge portfolios poses additional challenges due to limited economic and human resources at disposal by Bridge Authorities. Taking the Italian case as reference, the Italian Ministry of Infrastructures and Transportation (MIT) issued in 2020 the “Guidelines for risk classification, safety assessment and structural health monitoring of existing bridges” (MIT, 2020) to support road managers in this difficult task. The proposed multi-level approach for risk classification is based on data collection, visual surveys, preliminary risk assessment, detailed structural analyses and safety checks. Its application in the first phases has required the joint effort of road managers and scientific community as demonstrated by Consorzio FABRE activities on this topic (Meoni et al., 2025; Natali et al., 2023; Rossi et al., 2023; Salvatore et al., 2024; Santarsiero et al., 2021; Trifarò et al., 2024). The prioritization of accurate evaluations and maintenance/retrofit interventions by Italian Guidelines (IG), in the so-called Level 2, are based on an overall warning class. The rationale is that bridges classified to the highest classes are on the top of priority lists. However, alternative approaches should be explored for timely monitoring and appropriate intervention policies. At portfolio level, depending on the considered hazard, mechanical or analytical models are developed in form of archetype structures, as representative of a specific construction typology, governed by a predefined taxonomy. The advantage of macro taxonomy-based approaches (Pitilakis et al., 2014) is that they can be used to address the lack of detailed structure-level information by using taxonomy branches. These are groups of structures with similar characteristics that perform similarly under a specific hazard. Multiple representative structures are analyzed for each taxonomy branch to capture the variability of the behavior in each class and return a probabilistic assessment through fragility curves. The effectiveness of this method depends on several factors such as the complexity of the classification scheme (Stefanidou and Kappos, 2019), the available data (Abarca et al., 2022), the modelling approach and the type of numerical analysis. Moreover, material degradation as corrosion should be accounted for as it affects capacity of both superstructure (Miluccio et al., 2022; Nettis Al. et al., 2024) and piers (De Domenico et al., 2023; Di Mucci et al., 2025) in the case of traffic loads and seismic hazard, respectively. Recent advancements in Machine Learning (ML) algorithms have led to their increasing use for fragility assessment applications due to the ability of modelling