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.

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This work aims to investigate different predictive models for estimating the unconfined compressive strength and the maximum peak strain of non-structural recycled concretes made up by ceramic and concrete wastes. The extensive experimental campaign carried out during this research includes granulometric analysis, physical and chemical analysis, and compression tests along with the use of the 3D digital image correlation as a method to estimate the maximum peak strain. The results obtained show that it is possible to accurately estimate the unconfined compressive strength for both types of concretes, as well as the maximum peak strain of concretes made up by ceramic waste. The peak strain for mixtures with concrete waste shows lower correlation values.

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The mechanical behavior of test pieces extracted from two specimens of Pinus halepensis Mill., from the same geographical area and close to each other, was examined in this study. Using a methodology based on Digital Image Correlation (DIC) and implemented during compression strength testing, the modulus of elasticity in compression parallel to the grain (MOEc) was obtained. In addition, the value of compressive strength (MORc) was obtained for this type of wood. The research was complemented with a reliability study, determined using the Weibull modulus, from the MORc values. A microstructural and behavioral study of the most representative pieces after failure was also conducted to correlate breakage with the behavior of the pieces during the tests monitored by DIC, to link both studies. DIC was shown to be an ideal and low-cost technique for the determination of the studied properties, and obtained average values of MOEc of 50.72 MPa and MORc of 9693 MPa. These values represent fundamental data for design and calculations of wooden structures. A reliability value of between 11 and 12 was obtained using the Weibull modulus for this type of wood.

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