PSI - Issue 84

Federico Foria et al. / Procedia Structural Integrity 84 (2026) 296–303

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Fig. 4. Defects Index of tunnel sectors in the MIRET-Tunnel AI. (a) defect type M4: water lackage; (b) defect type M9: crack.

4.3. Bridge case study

The bridge case study concerns again a railway located in central Italy. In this regard, Figure 5a shows a point cloud as retrieved from the Mobile Mapping Survey, while Figure 5b a priority map extracted from MIRETS. Looking at the Italian Guidelines (HCPW, 2022a), it must be observed that the level of susceptibility associated with landslide risk depends on the geomorphological context in which the bridge is located. This information can be obtained through Level 0 census data and verified through Level 1 visual inspections. Where it is determined that the probability of a landslide affecting a given bridge is zero, it is not necessary to proceed with the Landslide Attention Class assessment, as it would not affect the determination of the overall Attention Class of the bridge. This is the case of the bridge under consideration; indeed, Figure 3b highlights that the area where the bridge is located does not suffer from landslides. Therefore, the solution is that the Landslide Attention Class is unclassified depending on the “evidence”. The fact that there is no evidence, confirmed by the geometric, geological and geomorphological surveys of the mapping, is in itself evidence on which we can base the assessment.