Response Surface Model of the Reduced Flexibility Matrix for Bayesian Damage Identification.

The present work is concerned with the use of a Response Surface Model of the reduced flexibility matrix for structural damage identification. A Response Surface Model (RSM) is fitted with the aim at providing a polynomial relationship between nodal cohesion parameters, used to describe the damage field within the structure, and elements of the reduced flexibility matrix. A design of experiment built on combinations of a relatively small number of nodal cohesion parameters is used to fit the RSM. The damage identification problem is formulated within the Bayesian framework and the Delayed Rejection Adaptive Metropolis method is used to sample the posterior probability density function of the uncertain cohesion parameters. Numerical simulations addressing damage identification in plates were carried out in order to assess the proposed approach, which succeeded in the identification of the different damage profiles considered. Besides, the use of a RSM, instead of a FEM of the structure, resulted in reductions of up almost 78% in the required computational cost.

Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders.

The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available. This work solves this inverse problem with an artificial neural network that receives these experimental data as input and outputs the thermalphysical properties of the adhesive layer, where defects can occur. An autoencoder is used to reduce the dimension of the transient 1D thermography data, where its latent space represents the experimental data in a lower dimension, then these reduced data are used as input to a fully connected multilayer perceptron network. Results indicate that this is a promising approach due to the good accuracy and low computational cost observed. In addition, by including different noise levels within a defined range in the training process, the network can generalize the experimental data input and estimate the positioning of defects with similar quality.