Italy, a nerve center for Western culture, holds the largest number of artistic and cultural assets declared
World Heritage by UNESCO. From the Romans to the present day, an ever-growing infrastructure
system, rich in tunnels, bridges and viaducts, has been the expression of a high engineering expertise.
For the management of the aforementioned complex infrastructure heritage, the development of
automated control and maintenance plans is one of the issues on which the engineering and research
community focuses its resources and efforts. In this study, an approach is proposed to automate the
process of classifying defects in tunnels using deep learning techniques to protect and maintain the
concrete tunnel lining. The acquisition of images from non-destructive monitoring techniques, such as
Ground Penetrating Radar, within a supervised learning process allows the creation of an effective tool
for the automatic detection of severe defects such as cracks, anomalies, and voids. The obtained results
provided for a high degree of accuracy in identifying the tunnels’ structural condition. The use of the
developed strategy, based on machine learning and non-invasive inspection techniques, is costeffective for infrastructure managers. Such a procedure reduces both the number of invasive
interventions on the tunnel lining and the time and cost associated with employing specialized
technicians