Structural health monitoring is a significant concern in the field of engineering and has a wide-ranging impact on various structures. In this context, it has become crucial to implement techniques that can detect defects early on and track their growth. The effective combination of the advantages of numerical simulations and physical measurements is the approach of dynamic data driven application systems (DDDAS), which essentially attempts to develop novel processes that enable real-time, dynamic information exchange between the physical system and its corresponding numerical simulator (referred to as a virtual twin) [1]. The coupling between measured data through digital image correlation (DIC) [2] or optical fibre sensors [3] and physics based constitutive modelling is generally performed by using the data to identify the parameters of a constitutive model with given structure. Amongst many methods that have been developed for inverse problems for parametric identification [4], the modified constitutive relation error (mCRE) [5] is utilised in this research as it gives a strong physical sense through the modelling error term of the functional to minimise. In this approach, the static and kinematic admissibilities are considered as the reliable set of equations, and the constitutive relations along with the equivalence of the measurement data with the model displacements are considered as unreliable and to be minimised. The mCRE essentially works on a sequential minimisation strategy where the optimal fields are calculated for a given parameter set by minimising the mCRE functional before minimising the obtained mCRE functional with respect to the model parameters [6].