ANIDIS - L'ingegneria Sismica in Italia, ANIDIS XIX & ASSISi XVII - 2022

Dimensione del carattere:  Piccola  Media  Grande

A new tailored developed software for the risk classification of bridges according to the Italian guidelines

Agnese Natali, Vincenzo Messina, Walter Salvatore, Vincenzo Gervasi, Davide Anzalone, Andrea Canciani, Fabio Severino

Ultima modifica: 2022-10-12


The “Italian guidelines for the maintenance of bridges” propose a method for the management and classification of risk of infrastructures. The method is structured in different levels. The first three ones (from level 0 to level 2) are applied to all the structures, and the final aim is to determine the Level of Attention (LoA) of the structure by adopting a qualitative method for the determination of structural, seismic, hydraulic and geotechnical risk (the higher is the LoA, the more critical are the level of risks obtained). The other levels (from level 3 to level 5) require a deeper level of analysis (the assessment of the level of safety of the structure and of the resilience of the network are involved), but are applied only to those structures which are characterized by higher LoAs. Focusing on the first three levels, they are based on: collecting all the data for deepening the knowledge of the bridge, especially focusing on those necessary for the evaluation of the LoA (Level 0); performing the visual inspections for all the risks to assess the conservation status of the structure and of its context (Level 1); determination of the LoA by applying the logical flows proposed by the method (Level 2). In this framework, this paper shows a new software that can support all the operations and gather the relevant data acquired and determined for each bridge and for each level of the method (from level 0 to level 2). This unique software contains all together: the important pieces of information collected in level 0 and 1 for the determination of the LoA; support the technician in the execution of the visual inspections of level 1 for all the risks; automatically determine the LoA, starting from the previously collected data and give the base for a final report about the risk classification of the structure. The input and output data of this software can be used to gather all the key pieces of information about the actual and current status of all the structures and can be used as a powerful instrument to support the organization of the operations and decisions after the determination of the LoA. This is achieved through an eXplainable Artificial Intelligence (XAI) algorithm, a new expert system that overcomes the usual pitfalls of conventional “black box” approaches. This improves transparency and trust in its results and consequently in the decision-making processes.

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