In the realm of structural design and assessment, numerical modelling tools have become indispensable, facilitating the analysis of complex structural components with diverse material properties, and loading scenarios while minimizing reliance on extensive experimental testing. However, existing structural modelling techniques, as finite element models, encounter challenges in accurately capturing the nuanced real-world behaviour of intricate structures, such as aircraft and space structures. Issues arise from factors like material property scatter, manufacturing-induced geometric deviations, residual stress, and other effects that are challenging to precisely estimate or fully capture during service. This communication assess the potential of digital twins in addressing these limitations through model updating techniques, leveraging full-field strain measurements. Through integration and processing of data from sensors, and other operational inputs, digital twins provide a comprehensive understanding of structural behaviour throughout a structure's life cycle. Exploiting machine learning techniques such as graphical neural networks (GNNs), recurrent neural networks (RNNs), and physics-informed neural networks (PINNs), this research introduces novel methods for model calibration and updating by combining experimental inputs with simulation models. Through the integration of sensor data and new updating techniques, digital twins have the potential to revolutionize the structural design and life cycle structural assessments. They are able to offer valuable and customized insights into the structural health, safety, and reliability structures, leading to more efficient structures.