Substructural damage identification in a digital twin framework using heterogeneous response reconstruction

The external excitations, interface forces and responses at the interface degrees-of-freedom are normally required in many existing substructural condition assessment methods, while they are difficult or even impossible to be accurately measured. To address this issue, a digital twin framework for output-only substructural damage identification with data fusion of muti-type responses is proposed in the present paper. First, heterogeneous responses including displacements, strains and accelerations from the target substructure are measured and divided into two sets. The multi-type responses in measurement set 2 are reconstructed with the first set of responses and transmissibility matrix in time domain. Then, a recovery method is introduced to obtain angular displacements from translational displacements and strains, to acquire angular accelerations from translational accelerations and the second order derivatives of strains by continuous wavelet transform. The recovered angular displacements and angular accelerations are involved into the evaluation of objective function. Besides, to avoid the single and monotonous search operation of traditional optimization algorithms, a reinforced learning-assisted Q-learning hybrid evolutionary algorithm (QHEA) by integrating Q-learning algorithm, differential evolution algorithm, Jaya algorithm, is developed as a search tool to solve the optimization-based inverse problem. The most suitable search strategy among DE/rand/1, DE/rand/2, DE/current-to-best/1, Jaya mutation in each iteration is selected and implemented under the guidance of Q-learning algorithm. Numerical studies on a three-span beam structure are performed to verify the effectiveness of the proposed approach. The results demonstrates that the proposed output-only substructural damage identification approach can accurately identify locations and severities of multiple damages even with high noise-polluted responses.