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Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

[+] Author Affiliations
Arvind Keprate

DNVGL, Høvik, Norway

R. M. Chandima Ratnayake

University of Stavanger, Stavanger, Norway

Shankar Sankararaman

SGT Inc., Moffett Field, CA

Paper No. OMAE2018-78608, pp. V004T03A029; 11 pages
doi:10.1115/OMAE2018-78608
From:
  • ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 4: Materials Technology
  • Madrid, Spain, June 17–22, 2018
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5123-4
  • Copyright © 2018 by ASME

abstract

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

Copyright © 2018 by ASME

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