Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network

There is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal pressure and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, based on Artificial Neural Networks, for the failure pressure prediction of pipe elbows subjected to combined loadings. The neural network was trained with data generated using the Finite Element Method. A parametric analysis was then carried out to study the failure behaviour of corroded high-strength steel subjected to combined loadings. It was found that defect depth, length, spacing (longitudinal), and axial compressive stress greatly influenced the failure pressure of a corroded pipe elbow, especially for defects located at the intrados, with reductions in failure pressure ranging from 12.56–78.3%. On the contrary, the effects of circumferential defect spacing were insignificant, with a maximum of 6.78% reduction in the failure pressure of the pipe elbow. This study enables the failure pressure prediction of corroded pipe elbows subjected to combined loadings using empirical equations. However, its application is limited to single, longitudinally interacting, and circumferentially interacting defects with the specified range of parameters mentioned in this study.