Bayesian Regression over Sparse Fatigue Crack Growth Data for Nuclear Piping


Lye, Adolphus ORCID: 0000-0002-1803-8344, De Angelis, Marco ORCID: 0000-0001-8851-023X and Patelli, Edoardo ORCID: 0000-0002-5007-7247 (2020) Bayesian Regression over Sparse Fatigue Crack Growth Data for Nuclear Piping. [Poster]

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Abstract

This work looks into the use of Bayesian Regression technique to quantify the uncertainty over a set of sparse fatigue crack growth data that is obtained through a 4-point bending test of a carbon-steel nuclear piping. As a form of validation, the Interval Predictor Model is used which also serves as a way to provide a "Reliability Certificate" for the Bayesian Regression results.

Item Type: Poster
Depositing User: Symplectic Admin
Date Deposited: 04 Nov 2020 15:27
Last Modified: 18 Jan 2023 23:23
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3106033
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