Speaker
Description
The management of the integrated water cycle relies on a complex network of installations (dams, reservoirs, pipelines, and treatment plants) that depends heavily on energy grids. Since these assets are highly vulnerable to both natural and anthropogenic hazards, implementing advanced, systemic monitoring strategies is essential to ensure their security and resilience.
Within this framework, the SCIRES project (Space-based Services to support resilient and sustainable Critical Infrastructure – Demonstration Project [1]) led by Planetek Italia alongside ENEA and INGV under the EISAC initiative, provides cutting-edge Earth Observation (EO) services.
SCIRES’s goal is to boost infrastructure resilience through three synergistic operational modules. Beside other activities, ENEA is involved in the Pipeline Monitoring Service, which detects early signs of leaks and structural issues in pipelines by combining satellite radar data (EO), IoT sensors, and AI algorithms.
ENEA HIFOS (Holographic Interferometry and Fiber Optic Sensors) laboratory implemented an IoT sensor network equipped with 32 Fiber Bragg Grating (FBG) sensors, arranged in 4 fiber optic chains (lines) on the Magliana (Rome, Italy) pipe-bridge over the Tiber river, ensuring comprehensive continuous coverage of critical structural areas. Data were post processed using specialized software to make them accessible and usable for structural engineers. The analysis demonstrated how the FBG technology responds to dynamic stimuli applied to the bridge when vehicles pass over it or the hydraulic pumps are operational.
The results are highly promising and pave the way for extending this technology to different bridges hosting oil pipelines, railways, motorways, essentially, anywhere continuous monitoring of structural parameters is critical to guaranteeing operational safety and service continuity. Furthermore, the integration of Deep Learning algorithms can easily detect of infinitesimal structural damage years before a crack becomes visible; thereby enabling fully automated predictive maintenance.