Carbon fiber reinforced polymer composites present excellent mechanical properties, however, their behaviour under fatigue and the interaction between the different failure modes is not yet well understood. This uncertainty, or lack of knowledge, is the reason why they are still not extensively used in the aerospace industry, where safety is critical. In this paper, Bayesian neural networks trained with approximate Bayesian computation (BNN by ABC-SS) are used to quantify such uncertainty and undertake a probabilistic safety assessment. An experiment is carried out using data from composite fatigue testing, where the proposed algorithm is compared against the state-of-the-art Bayesian neural networks. The results show that, the flexibility of BNN by ABC-SS to quantify the uncertainty significantly contributes towards a reliable safety assessment. Measuring the unknowns with confidence can be crucial when safety is at stake.
How to Cite
Bayesian Neural Networks, Composites, Uncertainty Quantification, Safety Assessment
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