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

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.