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In this paper, a hybrid utilisation of the continuum damage mechanics approach and some novel techniques was employed for progressive failure analysis of internally pressurised filament-wound composite tubes. The constructed numerical model in Abaqus software, which is employed for prediction of functional and burst failure of the tubes, was validated with an experimental failure evaluation available in the literature. Results show the high accuracy and precision of the proposed model. Therefore, this model can be used as a cost-effective virtual failure exam prior to experimental tests or as their alternative. Then, this validated model has been used in the second part of this paper for optimisation. Due to some drawbacks of the previous optimisation methods, by using the Taguchi approach capabilities, a novel strategy is proposed for optimisation of the stacking sequence of tubes under various stress ratios with taking into account the manufacturing winding angle restrictions. Results reveal that the obtained optimal stacking sequences are asymmetric and highly dependent on calculation strategies and applied stress ratio.

Induced uncertainties during the filament winding process may cause a significant stochastic variation in the mechanical behaviour of composite shells. This paper aims to develop a novel and deep uncertainty quantification (UQ), sensitivity and reliability analyses of filament wound shells considering manufacturing uncertainties. Firstly, a progressive damage analysis is performed to estimate their deterministic burst pressure. Then, a signal-to-noise (SNR) approach is employed using the Taguchi method for sensitivity analysis and screening uncertainties arising from manufacturing. Initial results reveal that the shells are more sensitive to thickness uncertainties for thinner structures. Then, probabilistic and reliability analyses are carried out using the Boosted Decision Trees Regression (BDTR) approach from machine learning algorithms. Despite the complexity and non-linear relationships in the problem, the developed BDTR-based metamodel shows powerful predictive performance. A comparative study shows that ply thickness uncertainty leads to a significant underestimation of failure probability. For expensive and time-consuming models in that only a few runs can be affordable, a modified approximation method for reliability analysis is proposed. Results indicate a high capability at estimating failure probability with high accuracy.