This work aims to develop an approach for the reliability-based analysis for the design and repair of pressurized pipes by means of composite solutions. To this end, the approach uses a simulation method to estimate the failure probability of the solution based on the Monte Carlo approach and a Polynomial Chaos Expansion surrogate metamodeling strategy. This combination allows us to reduce the computational time required for evaluating the system´s probability of failure as well as extracting the Sobol' indices during the sensitivity analysis stage. The uncertainties related with the composite solution were obtained by means of the Digital Image Correlation approach, allowing us to extract the Probabilistic Distribution Functions (PDF) of its main mechanical parameters. This methodology is validated through the design and repair of a pressurized pipe using a carbon fiber solution and roll wrapping technology. The results show the strong potential of the proposed methodology for the safety evaluation of pressurized composite pipes.

This article makes a comparison between different Digital Image Correlation methods to determinate the main mechanical characteristics of composite materials. More specifically Carbon Fiber Reinforced Polymers. For this purpose, several tensile tests were carried out using the same camera and lens model. Different statistical methods as well as probabilistic numerical simulations were performed with the aim of evaluating the discrepancies between methods, and between different mechanical parameters. We want to highlight the consistency of the results, enabling the possibility of using 3D methods with non-planar specimen for determining the mechanical properties of Carbon Fiber Reinforced Polymers. In this case, the novelty is focused on the use of different configurations (2D and 3D) to study the differences in terms of results. the objective is not the specific characterization of CFRP, but to analyze the way in which the use of a dataset from DIC3D or, on the contrary, from DIC2D affects the final results. According to this, it is possible to concluded that significative differences arise in the evaluation of the elastic properties that could be assigned to the uncertainties of the methods. However, this significance does not appear in the results of the probabilistic simulation.

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

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