Space-borne DInSAR measurements exploitation for risk classification of bridge networks

Abstract

Existing bridges constitute essential infrastructures of land transport and communications routes worldwide. They are often outdated and vulnerable; for this reason, monitoring and safety should be ensured for their use. The reduced economic and technical resources lead to the necessity of defining intelligent monitoring strategies for the preliminary classification of the infrastructures to establish an order of priority for executing more in-depth checks, verifications, and interventions. In this context, earth monitoring through satellite remote sensing has become a fundamental research topic in the last decades. This technique allows to obtain innumerable information on the temporal and spatial evolution of displacements at a territorial scale by means of the observation of wide deformation phenomena such as subsidence, landslides, and settlements. Furthermore, at a smaller scale, as in the case of a single bridge, the use of high spatial resolution and high sampling rate data could be crucial in civil engineering scenarios to carry on a preliminary structural monitoring of a road, railway network, or a single bridge. This work proposes a procedure for a large-scale analysis for the monitoring of an entire road network, based on remote sensing Structural Health Monitoring (SHM). The capability of the procedure is investigated on a network of 68 bridges, using deformation measurements derived from satellite remote sensing, where large stacks of ascending and descending Differential SAR Interferometry DInSAR data products were available. A Risk Class is estimated for each bridge based on the deformation analysis, considering the potential phenomena at both territorial and local scales. Based on such a Risk Class, the stakeholders can define most critical bridges as well as more in-depth monitoring strategies.