PSI - Issue 84

Federico Foria et al. / Procedia Structural Integrity 84 (2026) 304–312

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sought to identify quantitative parameters that can be measured using geomatic techniques integrated with direct in situ measurements, integrated into a workflow that allows for obtaining a semi-quantitative evaluation of the attention class for unlined tunnels comparable to that defined for lined tunnels.

Fig. 1. MIRET: Management Identification Risk for Existing Tunnels.

Depending on type and number of sensors, mobile mapping systems (MMS) provide a great number of parameters, such as geometry, humidity, lining characteristics, etc. The dense point cloud provided by MMS enables a virtual exploration of the tunnel and the extraction of key data regarding rock mass discontinuities, e.g. orientation, spacing, persistence, etc. which can be integrated with direct field observations for all those parameters that can be directly obtained from the point cloud processing, enabling the classification of the rock mass according to existing standards. The general structure adopted by the Italian guidelines for defining the Attention Class (CdA) is as follows:

Fig. 2. Attention class evaluation workflow according to Italian Guidelines (2022).

In this paper, we aim to offer an original contribution to the definition of the geological parameters that contribute to determining the Hazard and Vulnerability parameters within the framework of defining the Global Attention Class and the Local Attention Class.

2.1 Mobile Mapping: ARCHITA

The inspections are performed using ARCHITA, a system for the multi-dimensional mobile mapping of tunnels that integrate laser scanner, ground penetrating radars, and linear and thermal cameras on a bimodal vehicle with a survey speed of 15-30 km/h (Foria et al. 2019).