Advancing Bridge Health Monitoring through Satellite InSAR and Performance-Based System Design

Bridge infrastructure is a critical component of transportation networks, yet its management faces increasing challenges due to aging assets. Bridge Management Systems (BMS) aim to allocate resources efficiently by prioritizing repair and maintenance interventions based on structural condition assessment, which is usually derived from inspection reports. However, traditional condition ratings relying solely on visual inspections are often insufficient, as they lack quantitative evaluation of either mechanical properties of the structure, structural responses and applied loads. In this context, Structural Health Monitoring (SHM) offers a valuable opportunity for objective, timely and continuous assessment of structural performance. However, the high costs of permanent on-site SHM systems hinder their large-scale adoption, and the absence of standardized procedures for effective monitoring system design risks leading to inefficient use of resources through the installation of systems that provide useless information. As a result, SHM systems are currently usually installed only on a limited number of strategic or severely degraded bridges. Innovative techniques, algorithms and methods that enable network-scale and cost-effective monitoring therefore need to be explored and enhanced for potential integration in bridge management practice. This thesis proposes innovative solutions to overcome key obstacles to the widespread and systematic implementation of SHM in bridge management, by leveraging satellite SAR technology for device-free, large-scale, and inexpensive bridge monitoring, and a performance-based approach for monitoring system design. The main research contributions include: (i) an end-to-end application of satellite SAR technology to bridge monitoring, from the satellite data processing steps to the structural interpretation of results, advancing both the understanding and the practical applicability of SAR technology for infrastructure managers; (ii) a novel clustering algorithm for the automated classification of SAR-derived outputs, simplifying their rapid interpretation and enabling large-scale application of satellite monitoring; (iii) a closed-form expression for estimating the performance of SHM systems by predicting the structural state parameters uncertainty, which can be directly employed in the design of monitoring systems.