Implementation of a Digital Twin integrating remote sensing information for network-level infrastructure monitoring

Nowadays, there is an emerging demand from public authorities and managing bodies, to evaluate the overall health of infrastructures and identify the most critical transport assets. Considering the national-scale level, thousands of transport infrastructure are in critical conditions and require urgent maintenance actions. Currently, most of available Digital Twins (DT) allow to explore and visualize data including limited kind of information. This issue still limits the operative and practical use by infrastructure owners, that require fast solutions for managing several amount of data. Moreover, this idea is perfectly in line with European and national actions related to the development of a DT of the earth’s systems, including the “DestinE” programme of the European Commission by EUSPA and the European Space Agency (ESA). For this purpose, a dynamic DT model of a critical infrastructure is developed, using the available data about design information, historical maintenance operations and monitoring surveys based on satellite imageries. In this context, this study presents an innovative concept of Digital Twin, which integrates all the details coming from NDTs surveys, on-site inspections and satellite-based information, to store, manage and visualize valuable information. This is made possible by analysing the main several gaps and limitations of existing platforms, providing a viable integrated solution developing an upgradable strategic analysis tool. To this purpose, remote sensing methods are identified as viable technologies for continuous monitoring operations. More specifically, data coming from satellites and the processing techniques, such as the Multi-Temporal SAR Interferometry approach, are strategic for the continuous monitoring of the displacements associated to transport infrastructures. An advantage of these techniques is the lighter data-processing required for the assessment of displacements and the detection of critical areas [1, 2]. The study introduces two main levels of innovation. The first one is associated to the integrated approach for transportation planning, integrating quantitative data from multi-sources, into the more traditional territorial analysis models. The second one is related to the technological engineering discipline, and it consists of the fusion of observation data from multi-source, with the last-generation dynamic data connected to the environment.