Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R² of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results.

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

Most of the standards available for the assessment of the failure pressure of corroded pipelines are limited in their ability to assess complex loadings, and their estimations are conservative. To overcome this research gap, this study employed an artificial neural network (ANN) model trained with data obtained using the finite element method (FEM) to develop an assessment equation to predict the failure pressure of a corroded pipeline with a single corrosion defect. A finite element analysis (FEA) of medium-toughness pipelines (API 5L X65) subjected to combined loads of internal pressure and longitudinal compressive stress was carried out. The results from the FEA with various corrosion geometric parameters and loads were used as the training dataset for the ANN. After the ANN was trained, its performance was evaluated, and its weights and biases were obtained for the development of a corrosion assessment equation. The prediction from the newly developed equation has a good correlation value, R² of 0.9998, with percentage errors ranging from −1.16% to 1.78%, when compared with the FEA results. When compared with the failure pressure estimates based on the Det Norske Veritas (DNV-RP-F101) guidelines, the standard was more conservative in its prediction than the assessment equation developed in this study.

This paper describes the application of finite element method (FEM) to predict the failure pressure of corrosion-affected pipes, specifically for pipes with multiple internal (2 and 3 defects) longitudinally aligned interacting corrosion defects subjected to internal pressure and axial compressive stress. The FEM was verified against results of full-scale burst tests. The developed FEM incorporated material nonlinearity, which allows for large strains and displacements. Furthermore, true ultimate tensile strength (UTS) instead of the typical engineering UTS was employed to determine the point of failure. The pipes used in the finite element analysis (FEA) were modeled based on API 5L X52 with a length of 2000 mm, a nominal outer diameter of 300 mm, and a nominal wall thickness of 10 mm. The results obtained from the FEA were presented in a graph form and then used to determine the failure pressure trendlines of multiple longitudinally aligned interacting corrosion defects for varying defect depths and spacing between defects. In addition, the failure pressure trendlines of multiple longitudinally aligned interacting defects were compared to determine their differences. As expected, 3 longitudinally aligned interacting defects showed lower failure pressure compared to 2 longitudinally aligned interacting defects.

Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R²) value of 0.9930 and an error range of −10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80.

Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged from −6.33% to 2.39% when compared to an unseen FEA dataset, with a correlation value (R²) of 0.9975. A parametric study was subsequently performed using the equation to determine the effects of material property, defect depth, defect length, and longitudinal compressive stress on the remaining strength of pipelines with a single corrosion defect. Defect depth reduced the failure pressure by more than 65% on average, longitudinal compressive stress by more than 20% on average, and defect length by more than 21% on average.