Benefits of metamodel-reduction for nonlinear dynamic response analysis of damaged composite structures

• Localization and quantification of the damage in composite structures using artificial neural networks method (ANNs).
• The proposed method allows the reporting of the propagation and evolution of the damage in the composite structure with a high accuracy.
• A dynamic analysis response of locally damaged structure is investigated using a metamodel-reduction consists of Craig–Bampton method combined with ANNs.
• The proposed metamodel-reduction reduces significantly the computational costs.

In this paper, a novel method for damage prediction and dynamic behaviour analysis of laminate composite structures is proposed and investigated. The dynamic behaviour of transversely isotropic layers is expressed through elasticity coupled with damage using a phenomenological macro-model for cracked composite structures made of polymer reinforced with long glass fibres. The damage is fully described by a single scalar variable whose evolution law is expressed through the maximum dissipation principle. Using the classical linear Kirchhoff–Love theory of plates and considering the damage-induced nonlinearity, the obtained nonlinear dynamic equations are solved in time domain using a Newmark algorithm. To reduce the computational costs, a metamodel-reduction for damage localization and quantification is proposed where the Artificial Neural Networks (ANNs) and Craig–Bampton reduction methods are combined. Extracted stresses from finite element analysis are used as input for a feed-forward ANNs to estimate the damage severity. The Craig–Bampton reduction method is introduced as a Component Mode Synthesis (CMS) to investigate the case of assembled structures locally damaged. Numerical simulations show that the damage modifies significantly the dynamic properties restricted to the eigenfrequencies reduction. The designed feed-forward ANNs was verified and it provides promising results regarding severity and location of the damage. Moreover, the trained ANNs provide a quick response for damage level prediction in online procedure which permits to significantly reduce the computational costs.