Review of Multi-Scale Bridge Monitoring: An Information-Centric Framework Integrating Ground-Penetrating Radar, Distributed Optical-Fibre Sensing, and Satellite Remote Sensing for Bridge Digital Twins.

Bridge infrastructure worldwide is under increasing pressure from ageing assets, growing traffic demand, environmental deterioration, and the need for more effective lifecycle management. Although bridge-monitoring technologies have developed rapidly, much of the existing research has focused on individual sensing techniques, while the integration of heterogeneous information across different spatial scales has received comparatively less attention. This review takes an information-centric perspective on intelligent bridge monitoring and focuses on three representative technologies that provide complementary information at the material, structural, and network scales: Ground-Penetrating Radar (GPR) for assessing material condition, distributed optical-fibre sensing (DOFS) for monitoring structural response, and satellite remote sensing for providing network-scale information. Instead of comparing these technologies solely for their sensing principles, the review considers their complementary roles in bridge engineering and proposes a Material-Structural-Network (M-S-N) framework to organise monitoring information across scales. Recent developments in multi-source information fusion, AI-based interpretation, connected monitoring systems, autonomous monitoring, and self-evolving Digital Twins are also reviewed. Together, these advances indicate a shift from simply collecting monitoring data towards integrating information, extracting engineering knowledge, and supporting intelligent decisions. Based on this perspective, a Multi-Scale Integration Framework is proposed to illustrate how observations at different scales can be combined to provide a more complete understanding of bridge condition, structural performance, and infrastructure-network context. Future bridge monitoring should therefore move beyond isolated sensing and data collection towards multi-scale information generation and integration, supporting predictive maintenance, resilient asset management, and intelligent lifecycle decision-making across bridge networks.