Real-time digital twin updating strategy based on structural health monitoring systems

In structural health monitoring (SHM), model updating is concerned with identifying and updating system parameters (e.g. stiffness and mass) based on the measured response data of the monitored structure. With the increasing number of SHM systems deployed on modern structures in recent years, real-time model updating has become possible. This allows a digital twin of the monitored structure to be built, such that the structural behaviour can be monitored and predicted simultaneously throughout its life-cycle. In real applications, the structural response data are normally measured under operational conditions where the environment and loading condition cannot be directly controlled, which leads to significant identification uncertainty. The system model can also be complex, meaning that identifying system parameters directly from measured response data is challenging and time consuming. Focusing on the above concern, a real-time updating strategy for a SHM digital twin is proposed in this work. An intermediate model is used for environmental condition estimation and divergence analysis in order to increase the updating efficiency. A Bayesian system identification approach is adopted so that the identification uncertainty can be fully accounted for. Synthetic and laboratory examples are presented to illustrate the proposed updating strategy.