Journal of Pipeline Science and Engineering (Dec 2022)
Artificial neural network models of burst strength for thin-wall pipelines
Abstract
Burst strength is critical to pipeline design, operation, and integrity management. The Barlow formula with the ultimate tensile stress (UTS) was often used to estimate burst strength of line pipes. To consider the plastic flow effect of pipeline steels, an average shear stress yield criterion was proposed, and the associated Zhu-Leis solution of burst strength was obtained for defect-free pipelines in term of UTS and strain hardening exponent, n, of materials. The Zhu-Leis solution was validated by more than 100 burst tests for various pipeline steels. The Zhu-Leis solution, when normalized by the Barlow strength, is a function of strain hardening rate, n, only. In contrast, experimental burst test data, when normalized by the Barlow strength, are a weak function of UTS and pipe diameter to thickness ratio D/t, in addition to be the function of n. Due to the difficulty of obtaining a closed-from solution using three-parameter regression, machine learning technology is adopted to develop alternative models of burst strength based on a large database of full-scale burst tests. In comparing to the regression, the machine learning method works well for both single and multiple parameters by introducing an artificial neural network (ANN), activation functions and learning algorithm for the network to learn from training data and then make predictions. Three ANN models were developed in this paper for predicting the burst strength of defect-free pipelines. Model 1 has one input variable and one hidden layer with three neurons; Model 2 has three input variables and one hidden layer with five neurons; and Model 3 has three input variables and two hidden layers with three neurons for the first hidden layer and two neurons for the second hidden layer. These three ANN models were then validated by the full-scale test data and evaluated through comparison with the Zhu-Leis solution and the linear regression results. On this basis, the best ANN model was recommended.
