Bayesian inference in validation of global MPP for the reliability analysis of composite structures
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Realistic analysis of structure failures under uncertainties are due to be associated with the use of probabilistic methods. One of the main problems in structural reliability analysis of composite laminate structures is the possible existence of multiple MPP (Most Probable Failure Point). In this work, we propose a numerical inverse technique for the global MPP search as a function of the anisotropy of laminated composites. Therefore, the analysis considers the maximum loading capability of the composite structure for a prescribed reliability level. This is equivalent to solving a target reliability-based design optimization problem. A Bayesian method to estimate the probability of failure based on Monte Carlo simulation provides the validation of the results. The validation process demonstrates that the proposed methodology is adequate to estimate the probability of failure of the laminated composite structures. Furthermore, the paper outlines and discusses the sensitivity of reliability index under maximum loading variability for angle ply composites.
KeywordsUncertainty Reliability Composite structures Inverse optimization Bayesian estimation Sensitivity
The authors acknowledge the financial support provided by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the funding of “The Associate Laboratory of Energy, Transports and Aeronautics (LAETA)”.
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