Risk evaluation of transportation network system with spaceborne synthetic aperture radar technology

Abstract

Spaceborne Synthetic Aperture Radar (SAR) is a non-contact remote sensing technology that detects surface deformation by analyzing the phase differences between radar images acquired over the same area at different times. Due to its extensive coverage, high spatial resolution, and all-weather operational capability, spaceborne SAR has become an established technique for large-scale, continuous monitoring of civil infrastructure. Transportation networks constitute a fundamental component of urban infrastructure, playing a pivotal role in enabling efficient mobility and fostering regional economic development. Extreme weather events severely threaten the durability and operational safety of transportation networks. However, limited funding restricts the deployment of traditional sensors for detailed and comprehensive monitoring of the entire transportation network system. In this research, a stack of Sentinel SAR images acquired over a two-years period is collected from the Copernicus Data Space Ecosystem, and subsequently processed with Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. A dedicated post-processing procedure, consisting of PS points refinement and clustering analysis, is applied to the displacement time series derived from the PS-InSAR processing. Then, statistical control limits method is employed to evaluate the risk levels across transportation network. Finally, the reliability and effectiveness of the proposed risk assessment framework are validated through a specific bridge case study. These findings demonstrate the potential of the proposed framework for large-scale, risk-informed assessment of transportation networks, thereby contributing to more proactive and data-driven transportation network management strategies.