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Research Papers: Materials Technology

Comparison of Various Surrogate Models to Predict Stress Intensity Factor of a Crack Propagating in Offshore Piping

[+] Author and Article Information
Arvind Keprate

Department of Mechanical and Structural
Engineering and Material Science,
University of Stavanger,
Stavanger 4036, Norway
e-mail: arvind.keprate@uis.no

R. M. Chandima Ratnayake

Department of Mechanical and Structural
Engineering and Material Science,
University of Stavanger,
Stavanger 4036, Norway
e-mail: chandima.ratnayake@uis.no

Shankar Sankararaman

NASA Ames Research Center,
SGT Inc.,
Moffett Field, CA 94035
e-mail: shankar.sankararaman@nasa.gov

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received April 10, 2017; final manuscript received July 13, 2017; published online August 16, 2017. Assoc. Editor: Hagbart S. Alsos.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.

J. Offshore Mech. Arct. Eng 139(6), 061401 (Aug 16, 2017) (10 pages) Paper No: OMAE-17-1055; doi: 10.1115/1.4037290 History: Received April 10, 2017; Revised July 13, 2017

This paper examines the applicability of the different surrogate-models (SMs) to predict the stress intensity factor (SIF) of a crack propagating in topside piping, as an inexpensive alternative to the finite element methods (FEM). Six different SMs, namely, multilinear regression (MLR), polynomial regression (PR) of order two, three, and four (with interaction), Gaussian process regression (GPR), neural networks (NN), relevance vector regression (RVR), and support vector regression (SVR) have been tested. Seventy data points (consisting of load (L), crack depth (a), half crack length (c) and SIF values obtained by FEM) are used to train the aforementioned SMs, while 30 data points are used for testing. In order to compare the accuracy of the SMs, four metrics, namely, root-mean-square error (RMSE), average absolute error (AAE), maximum absolute error (MAE), and coefficient of determination (R2) are used. A case study illustrating the comparison of the prediction capability of various SMs is presented. python and matlab are used to train and test the SMs. Although PR emerged as the best fit, GPR was selected as the best SM for SIF determination due to its capability of calculating the uncertainty related to the prediction values. The aforementioned uncertainty representation is quite valuable, as it is used to adaptively train the GPR model (GPRM), which further improves its prediction accuracy and makes it an accurate, faster, and alternative method to FEM for predicting SIF.

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References

Figures

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Fig. 1

Methods for determining SIF (Adapted from Ref. [25])

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Fig. 2

Schematic of crack geometry on offshore piping

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Fig. 3

Schematic of a neural network. Adapted from Ref. [33].

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Fig. 4

Schematic showing loss function and slack variable in SVR. Adapted from Ref. [36].

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Fig. 5

Schematic of plate and crack geometry used in the case study

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Fig. 6

FEM model of plate and crack geometry used in the case study

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Fig. 7

Plots indicating relation between load, a, c, and SIF

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Fig. 8

Flowchart to build SM for SIF prediction

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Fig. 9

Comparison of SIF values obtained from ANSYS, BS7910, and different SMs

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