Decision support system for infrastructure safety based on innovative technologies, artificial intelligence, and Bayesian networks

The Research Project aims to extend structural health monitoring from a few strategical civil infrastructures to a wide set of civil infrastructures managed by infrastructure operators while making it economically sustainable for them. It aims to use satellite real-time Synthetic Aperture Radar (SAR) images of the Earth's surface to monitor the long-term structural response of bridges as well as the safety of the surrounding territory. Indeed, civil infrastructures are currently monitored by networks of sensors like accelerometers, inclinometers, and strain gauges, which provide accurate measurements but are very expensive and spot-wise. Therefore, operators can control only a limited number of bridges with monitoring systems and manage only a few strategic structures based on real-time information.

The other structures are managed based on reports from annual visual inspections, which however cause traffic interruptions and cannot identify some defects (e.g., loss of tension and corrosion in the prestressing cables). In contrast, satellite technologies already provide a large amount of information on the Earth’s surface, which are currently used to monitor oil exploration, subsidence assessment, and hydrogeological problems. Our idea is to use the satellite Interferometric Synthetic Aperture Radar (InSAR) to monitor a wide infrastructure stock. We will cope with its limits in terms of measurement frequency and accuracy by merging satellite data with those acquired by terrestrial-monitoring systems already installed on strategic structures or validated for the first time in the operational environment during this project. 

All information will be integrated and analysed within a prototype of the Decision Support System (DSS) through algorithms trained to update digital twins of bridges, recognize unexpected structural responses due to damage conditions or an incipient collapse, and provide early warnings. The DSS will suggest the optimal maintenance, repair, and rehabilitation strategy of the entire infrastructural asset according to principles of the expected utility theory. The Projects will improve users’ safety, minimize traffic jams, and reduce management costs.

The Project is funded by the University of Trento and Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto, grant number 2021.0224. Its duration is two years, from 15/09/2021 to 14/09/2023. The main deliverable is a prototype of a Decision Support System (DSS) for bridge management based on hybrid structural health monitoring: a combination of terrestrial (traditional on-site) and satellite technologies. Industrial partners are A22 Autostrada del Brennero SpA., Provincia Autonoma di Trento APOP, Nplus Srl.

Research group

  • Daniel Tonelli is the PI of this project. He achieved his Doctorate in Civil Engineering at the University of Trento, Italy, in 2020; his thesis focused on civil infrastructure management based on structural health monitoring. Currently, he is a postdoc at the University of Trento, Italy. His research includes structural health monitoring, infrastructure management, and Bayesian data analysis. He is involved in many research projects on remote monitoring, such as MITIGO and DPC-RELUIS WP6.
  • Valeria Francesca Caspani achieved her master’s degree in Structural Civil Engineering at the University of Trento, Italy, in 2021. Currently, she is a PhD student at the University of Trento, Italy. Her research includes remote sensing, statistical learning, and temperature compensation. She is involved in many research projects on remote monitoring, such as MITIGO and DPC-RELUIS WP6.
  • Stefano Zorzi achieved his master's degree in Structural Civil Engineering at the University of Trento, Italy, in 2022. Currently, he is enrolled in the Honours programme "Advanced Methods in Engineering" offered by the University of Trento. His research interests include structural health monitoring, Bayesian data analysis, statistical learning, decision making and decision support system for civil infrastructure.
  • Riccardo Torboli achieved his master's degree in Civil Engineering at the University of Trento, Italy, in 2022. His master's is titled “Structural health monitoring of civil infrastructures based on satellite measurement and SAR technology”. Currently, his collaboration with the research group is continuing.
  • Daniele Zonta achieved his Doctorate in Structural Mechanics at the University of Bologna in 2000. He is a Professor of Structural Engineering at the University of Trento, Italy. His research activity includes Infrastructure Management; Structural Health Monitoring; Sensor and Information Technology. His research is carried out in collaboration with major European infrastructure operators: Network Rail, Transport Scotland, Highway England, Brenner Basistunnel, Rete Ferroviaria Italiana, Brenner Autobahn, Autostrade per L’Italia.
  • Alfonso Vitti achieved his Doctorate in Environmental Engineering at the University of Trento in 2008. He has got specific knowledge and expertise in variational segmentation of signals, images, and digital elevation models; in geospatial data analysis and applications in environmental fields; in GNSS positioning and reflectometry, and the exploitation of SAR data for environmental applications. He is co-investigator of ESA TPM Project Proposal 64853 for
    ICEYE data: “Potentiality of ICEYE X-band SAR imagery for mapping space-time homogeneous areas to improve detection of superficial soil moisture changes in agricultural areas".

Recent publications

  • V. Caspani, D. Tonelli, R. Torboli, S. Zorzi, D. Zonta (2022). Remote monitoring of bridge displacements with satellite SAR technology. 8WCSCM, Orlando (FL), USA.
  • M. F. Bado, D. Tonelli, F. Poli, D. Zonta, & J. R. Casas (2022). Digital twin for civil engineering systems: An exploratory review for distributed sensing updating. Sensors, 22(9),
  • V. F. Caspani, D. Tonelli, F. Poli, & D. Zonta (2022). Designing a structural health monitoring system accounting for temperature compensation. Infrastructures, 7(1),