PSI - Issue 64

Carmine Lupo et al. / Procedia Structural Integrity 64 (2024) 645–652 Lupo C., Petti L. & De Gaetano C.M. / Structural Integrity Procedia 00 (2019) 000 – 000

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1. Introduction Several recent disaster revealed the need to develop and apply strategies and methods to monitor, control and evaluate the health state of the structures. In this context, transportation networks assume particular relevance. The absence or inadequacy of a transportation route can have a significant impact on the economic and social development of an urban area, both in emergency situations and under ordinary operating conditions. For example, if the only road connecting an urban area to the nearest hospital is closed, it can become a major issue for the entire region. This delay in intervention time can cause serious problems in case of emergency. In this context, the weak point is surely associated with structures like bridges, viaducts, and tunnels. Currently, managing road assets presents a complex set of challenges. In fact, most of the Italian road assets are outdated and have deficiencies due to a lack of maintenance and/or other factors. The most significant period of road construction in Italy occurred in the years following the Second World War, during which more than 50% of the bridges were built before 1982. Each road was designed and constructed taking into account the environmental factors of the crossed areas and the construction standards in force at the time. Additionally, even for bridges located along the same route, there can be significant disparities in terms of materials, geometric dimensions, structural configurations, and construction details. Unfortunately, such older structures are frequently devoid of comprehensive design documentation. This scenario introduces a potential challenge in the establishment of a robust and effective standard Bridge Management System (BMS). The use of an integrated BMS that use digital tools allows to manage the infrastructure more efficiently and in a sustainable way. A BMS is a system composed by several tools used for the management of a bridge stock. Traditionally, BMSs incorporate several components such as inventory, inspection, bridge condition, costs, and maintenance options. With technology development, BMSs have also benefited from digitalizing the processes and the implementation of innovative tools (Dayan V. et al (2022)). In particular, the BMS uses Building Information Modeling (BIM) tools, Geographic Information Systems (GIS) database, digital libraries, IoT technologies, machine learning, AI applications and data elaboration/visualization. In this context, in Italy a fundamental step forward has been made in 2018 with the creation of the National Information Archive of Public Structures (AINOP), see Ministry of Infrastructure and Transport (2018), and in 2020 with the adoption of the “ Guidelines for Risk Classification and Management, Safety Assessment and Monitoring of Existing Bridges” (LG20 ). The LG20 aim to standardize criteria for monitoring, assessing structural safety, and classifying the risk of existing bridges. In particular, it has been developed to consider bridge vulnerability, environmental hazards (including seismic activity, landslides, flooding, etc.) and exposure when evaluating infrastructural risk. The guidelines aim to quickly identify road infrastructures that require priority intervention, thus ensuring resource savings in terms of workers, money, and time, see Petti et al. (2023), Santarsiero G. et al. (2021), Capogna M. et al. (2023) and Buratti G. et al. (2022). The key point of the approach is the Level 2 (L2), i.e. the definition of the Attention Classes (CdA). The CdA is an index, defined for each bridge, which is a function of the hazard, vulnerability and exposure factors, and is classified into high, medium-high, medium, medium-low and low. In particular, the main goal of L2 consists in the definition of an Overall CdA, which is a function of other four CdA, one for each type of risk, and in particular: • Structural and Foundational : considers the main parameters influencing structural behavior in its usual operating conditions; • Seismic : takes into account the main parameters that influence the response to seismic actions of bridges and the road network to which they belongs; • Landslide : takes into account some specific parameters that indicate the level of involvement of the structure in any landslide phenomena, both from a spatial and temporal point of view; • Hydraulic : considers several specific parameters representing the involvement of the structure, both from a spatial and temporal point of view, to the hydraulic risk of river crossings. The achievement of a certain level of the CdA can implies a consistent action in terms of investigation, monitoring and control, see Ministry of Infrastructure and Transport (2020) and Ministry of Infrastructure and Transport (2022). In 2020, the Inter-University Research Center for the Prediction and Prevention of Major Hazards (CUGRI) started a joint project of Applied Research with the Southern Highways Company (SAM), and currently ongoing with the