ABSTRACT
Ageing infrastructure systems are easily prone to failure demanding frequent monitoring despite costs and risks. Digital twin offers an intelligent approach to overcome such limitations of traditional bridge monitoring. Current study focuses on implementing remotely connected real-time virtual models of bridges to visualize any induced stresses/strains of the actual bridge via the digital twin concept. It was first proposed to instrument a bridge with sensors and to follow an inverse approach to simulate the overall behavior using sensor measurements. A machine learning algorithm based finite element model has been suggested for this and three different algorithms were tested. All three machine learning algorithms accurately represented the behavior of the actual bridge in real-time. Overall, it was concluded that a combination of both physics-based and data-driven model development techniques is highly effective for real-time analyses and for the digital twining process. The proposed method can be extended to a large-scale bridge easily.
