Probabilistic Safety Assessment in Composite Materials using BNN by ABC-SS
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
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.