An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

Abstract

Conventional pipeline corrosion assessment methods for failure pressure prediction do not account for interacting defects subjected to internal pressure and axial compressive stress. In any case, the failure pressure predictions are conservative. As such, numerical methods are required. This paper proposes an alternative to the computationally expensive numerical methods, specifically an empirical equation based on Finite Element Analysis (FEA). FEA was conducted to generate training data for an ANN after validating the method against full scale burst test results from past research. An ANN with four inputs and one output was developed. The equation was developed based on the weights and biases of an ANN model trained with failure pressure from the FEA of a high toughness pipeline for various defect spacings, defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials, with a R2 value of 0.99. Extensive parametric study was subsequently conducted to determine the effects of defect spacing, defect length, defect depth, and axial compressive stress on the failure pressure of the pipe. The results of the empirical equation are comparable to the results from numerical methods for the pipes and loadings considered in this study.

Empirical modeling of stress concentration factors using finite element analysis and artificial neural networks for the fatigue design of tubular KT‐joints under combined loading

“…A promising tool is artificial neural network (ANN), a machine learning tool that can efficiently correlate the input and output of complex systems. 17 Its use has been rapidly increasing in all disciplines, including offshore structure design. Vijaya Kumar et al 18 revealed the capabilities of ANN integrated with the FEM to approximate complex phenomenon effectively.…”

“…New tools must be considered to establish a better correlation between the input variables and the SCF. A promising tool is artificial neural network (ANN), a machine learning tool that can efficiently correlate the input and output of complex systems 17 . Its use has been rapidly increasing in all disciplines, including offshore structure design.…”