Scour has become one of the most significant hazards affecting masonry bridges. Existing scour monitoring techniques often fail to meet the requirements for continuous and time-domain tracking of scour evolution. Moreover, existing scour early-warning systems predominantly rely on threshold-based risk assessment and management frameworks. A critical limitation of these systems is the difficulty in accurately defining threshold values, which are often derived from historical monitoring experiences. Given the variability in foundation conditions and river scour characteristics across different bridges, standardized threshold setting is highly challenging. Furthermore, threshold determination often lacks correlation with the health condition of the superstructure, which is an aspect engineers care most about. These deficiencies highlight the urgent need for a more intelligent monitoring framework that can integrate multiple monitoring techniques and facilitate interactive associations between monitoring data and the health condition of the superstructure.

This study explores the use of digital twin (DT) technology to overcome the shortcomings of current monitoring and maintenance strategies. By integrating real-world monitoring measurements with finite element modeling, the DT framework provides the opportunity to simulate "what-if" scenarios under high-fidelity conditions. Such advancements offer novel prospects for detecting scour-induced damage and intervening for the maintenance. This study utilizes DT technology within the context of a scour monitoring project for a masonry bridge in Northern Ireland, United Kingdom. A digital twin-based SHM and maintenance framework is developed to achieve seamless communication between the virtual model and the physical structure using sensor data. The developed model addresses limitations associated with traditional monitoring and maintenance approaches and demonstrates the potential of digital twins in forward model calibration and backward decision-making.