Damage Detection in Composites by AI and High-Order Modelling Surface-Strain-Displacement Analysis

Abstract

AbstractIn the recent years, machine learning algorithms have been widely employed for structural health monitoring applications. As an example, Artificial Neural Networks (ANN) could be useful in giving a precise and complete mapping of damage distribution in a structure, including low-intensity or localized defects, which could be difficult to detect via traditional testing techniques. In this domain, Convolutional Neural Network (CNN) are employed in this work along with one-dimensional refined models based on the Carrera Unified formulation (CUF) for surface strain/displacement based damage detection in composite laminates. A layer-wise kinematic is adopted, while an isotropic damage formulation is implemented. In detail, CUF-based finite element models have been exploited in combination with Monte Carlo simulations for the creation of a dataset of damage scenarios used for the training of the CNN. Therefore, the latter is fed with images of the strain or displacement field in a region of particular interest for each sample, which are subjected to the same boundary conditions. The trained CNN, given the strain/displacement mapping of an unknown structure, is therefore able to detect and classify all the damages within the structure, solving the so-called inverse problem.KeywordsDamage detectionArtificial intelligenceHigher-order finite elementsCarrera Unified Formulation