A Physics-Data Hybrid Framework to Develop Bridge Digital Twin Model in Structural Health Monitoring
Abstract
Digital twin aims to create a virtual model for a physical structure by combining measurement data in structural health monitoring. The most important feature is to achieve the physical structure-monitoring data synchronization. For this purpose, a physics-data hybrid framework to develop the bridge digital twin model in structural health monitoring is proposed in the paper. The physical base is firstly formed by the finite element model of the digital representation for the physical bridge that can fully incorporate both structural geometry and structural state. The data base is then built by all measurement data of the monitored bridge. By defining the context that is common to both physical base and data base, the mirror relationship between physical base and data base for the specified context is formulated. To achieve the best matching of the mirror relationship by minimizing process, the digital twin model in terms of the specified context can be developed. In such a way, the proposed framework integrates physical knowledge and data intelligence into one model. A demonstration of a simulated simply supported beam is provided to show how the digital twin model is developed by using proposed physics-data hybrid framework. It is found that the generated digital twin model is consistent with the current structural state of the beam. The presented physics-data hybrid framework helps in clearer understanding of the realization of digital twin model in structural health monitoring, providing a new perspective for smart bridge solutions.
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