The knowledge, the preservation and the maintenance of heritage infrastructures is one of the most challenging matters facing modern civilization. It involves, in inextricable patterns, factors belonging to different fields (cultural, humanistic, social, technical, economical, administrative) coupled with the requirements of safety that can be in conflict with the integrity of part of the infrastructure. For these reasons, it is fundamental to carry out investigations and new planning strategies to know and predict the conditions of very old structures. The paper focused on heritage railway tunnels, one of the most crucial elements of the railway infrastructures in Europe. ETS Srl introduced a new method for diagnostic of existing tunnels through multi-dimensional mobile mapping systems, and a new approach for the Management and Identification of the Risk for Existing Tunnels. The approach belongs to the digital strategies for infrastructure maintenance that are very fast and minimally invasive. The integrated instrumentation allows to have almost all the information necessary for the diagnostics of a structure with non-destructive tests, preserving the integrity of very old structures in a phase of preliminary assessment. In such a way, the process of visual inspection is automatized and back-officed. The results, in terms of defects on the structures, are digitalized and manipulated in different IT environments. The results can be incorporated in the information modelling and virtual reality inspections. The use of artificial intelligence will be necessary to speed-up the back-office phase and introduce the technologies as a new inspection standard. A case study for the application is presented through the methodologies, including some preliminary applications of AI algorithms for the detection of water defects.
Digital transformation in the visual inspection of heritage railways tunnels: Technology, artificial intelligence and methodology / Foria, Federico; Miceli, Gabriele; Calicchio, Mario; Manalo Catigbac, Gizelle; Loprencipe, Giuseppe. - (2022), pp. 337-348. [10.1201/9781003308867-20].
Digital transformation in the visual inspection of heritage railways tunnels: Technology, artificial intelligence and methodology
Giuseppe Loprencipe
2022
Abstract
The knowledge, the preservation and the maintenance of heritage infrastructures is one of the most challenging matters facing modern civilization. It involves, in inextricable patterns, factors belonging to different fields (cultural, humanistic, social, technical, economical, administrative) coupled with the requirements of safety that can be in conflict with the integrity of part of the infrastructure. For these reasons, it is fundamental to carry out investigations and new planning strategies to know and predict the conditions of very old structures. The paper focused on heritage railway tunnels, one of the most crucial elements of the railway infrastructures in Europe. ETS Srl introduced a new method for diagnostic of existing tunnels through multi-dimensional mobile mapping systems, and a new approach for the Management and Identification of the Risk for Existing Tunnels. The approach belongs to the digital strategies for infrastructure maintenance that are very fast and minimally invasive. The integrated instrumentation allows to have almost all the information necessary for the diagnostics of a structure with non-destructive tests, preserving the integrity of very old structures in a phase of preliminary assessment. In such a way, the process of visual inspection is automatized and back-officed. The results, in terms of defects on the structures, are digitalized and manipulated in different IT environments. The results can be incorporated in the information modelling and virtual reality inspections. The use of artificial intelligence will be necessary to speed-up the back-office phase and introduce the technologies as a new inspection standard. A case study for the application is presented through the methodologies, including some preliminary applications of AI algorithms for the detection of water defects.File | Dimensione | Formato | |
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