Comparing Different Metamodelling Approaches to Predict Stress Intensity Factor of a Semi-Elliptic Crack

Abstract

This paper examines the applicability of the different meta-models (MMs) to predict the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating in topside piping, as an inexpensive alternative to the Finite Element Methods (FEM). Five different MMs, namely, multi-linear regression (MLR), second order polynomial regression (PR-2) (with interaction), Gaussian process regression (GPR), neural networks (NN) and support vector regression (SVR) have been tested. Seventy data points (SIF values obtained by FEM) are used to train the aforementioned MMs, while thirty data points are used as the testing points. In order to compare the accuracy of the MMs, four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (AAE), and Coefficient of Determination (R2) are used. Although PR-2 emerged as the best fit, GPR was selected as the best MM 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, which further improves its prediction accuracy.