MT-InSAR Assessment of Bridge Monitoring Using Finite-Element Analysis under Thermal and Traffic Loads
Article
Khan, AQ, Andrawes, B. (2026). MT-InSAR Assessment of Bridge Monitoring Using Finite-Element Analysis under Thermal and Traffic Loads
. JOURNAL OF BRIDGE ENGINEERING, 31(7), 10.1061/JBENF2.BEENG-7850
Khan, AQ, Andrawes, B. (2026). MT-InSAR Assessment of Bridge Monitoring Using Finite-Element Analysis under Thermal and Traffic Loads
. JOURNAL OF BRIDGE ENGINEERING, 31(7), 10.1061/JBENF2.BEENG-7850
This study aims to demonstrate the feasibility of using cost-effective, freely available satellite imagery to achieve reliable, long-term bridge health monitoring. A three-span Warren through truss bridge (IL-251) in Peru, Illinois, is selected as a case study, in which Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) analysis is integrated with finite-element analysis (FEA) to monitor line-of-sight displacements over 9 years. A total of 223 freely available Sentinel-1A satellite images, acquired across three intervals (2015-2017, 2019-2021, and 2022-2024), are processed using permanent scatterer interferometry to extract midspan displacements. Furthermore, a detailed FEA of the bridge is conducted to predict the bridge's thermal displacements. The MT-InSAR results demonstrate strong seasonal thermal deformation trends, with displacements ranging from approximately -6 mm during summer to +4 mm during winter, confirming the structural stability of the bridge over a 9-year monitoring period. While both the MT-InSAR and FEA capture the general seasonal trend, deviations up to 6.7 mm between MT-InSAR and thermal-only FEA results are observed under certain conditions. To investigate these discrepancies, the effect of vehicular live load is considered. Additional FEA simulations incorporating estimated live loads based on traffic volume explain the remaining differences. Furthermore, classifying MT-InSAR measurements by traffic conditions improved the correlation between displacement and temperature, reaching R values up to 0.89. The findings demonstrate that freely available satellite imagery offers a scalable and low-cost method for reliable long-term bridge health monitoring under both environmental and operational loads.
