Hybrid monitoring methodology: A model-data integrated digital twin framework for structural health monitoring and full-field virtual sensing

This study presents a structural full-field sensing framework consisting of multi-type and multi-scale models. The digital twin is established by integrating finite element models (FEMs), bridge information model (BRIM), and internet of things (IoT). Multi-scale FEMs are built as the mechanical twin model to recognize the hidden features and information from the monitored data, where the hybrid monitoring methodology is adopted to realize the conversion, expansion, and fusion of data. Meanwhile, the BRIM serves as the data twin model to collect the lifecycle information from designing to operation, where a data visualization interface is developed to present heterogeneous information. Moreover, the physical entity and cyberspace are connected and interact via the IoT system. A paradigm is finally developed for intelligent big-data management and heterogeneous data fusion in structural health monitoring. It is applied to a three-span continuous model bridge for verification.