Digital Twin Technologies for Structural Health Monitoring

Civil infrastructure such as bridges, buildings, tunnels and dams experiences performance degradation during long-term service because of loading actions, environmental attack and material ageing, and therefore requires continuous structural health monitoring (SHM). Conventional monitoring, however, remains inadequate in terms of data integration, model updating and the linkage with maintenance decisions. This paper adopts a literature review and typical case analysis approach to examine the technical foundations of digital twins (DTs) and their main applications in real-time structural condition monitoring, damage identification and condition assessment, and predictive maintenance and decision support. The analysis indicates that, by connecting physical structures, digital models and real-time monitoring data, DTs can integrate multi-source information from sensor measurements, manual inspections, images, point clouds and environmental and load data. They can also support the dynamic representation of structural condition, anomaly identification and model parameter updating, and provide a basis for safety warning, inspection scheduling and maintenance planning. DTs thereby enhance structural condition monitoring capability and promote the shift of maintenance practice towards condition-based maintenance. At the same time, practical applications remain constrained by data quality, model accuracy, real-time synchronisation and engineering validation, and existing studies are mostly concentrated in the operation and maintenance stage and are based on prototype systems and single case studies. Future efforts are needed in multi-source sensing and data quality control, the integration of physics-based and data-driven methods, standardised data interfaces, lightweight models and whole-life-cycle applications.