Abstract
Dent-gouges as a result of the mechanical damage have serious implications for the burst capacity of oil and gas pipelines. The burst capacity of pipelines containing dent-gouges is lower than that of the same plain dented pipelines without gouges and that of the same gouged pipelines without dents. The well-known burst capacity prediction model adopted by the European Pipeline Research Group, i.e. the EPRG model, results in predictions of the burst capacity with high variability. In this study, a machine learning tool is employed to improve the predictive accuracy of the EPRG model for pipelines containing dent-gouges. To this end, a relatively large number of full-scale burst tests of pipe specimens containing dent-gouges are collected from the literature. The Gaussian process regression (GPR) technique, which is a class of non-parametric Bayesian model widely used in the machine learning, is employed to improve the EPRG model based on the collected full-scale burst test data. The full-scale burst tests are used to evaluate the hyper-parameters involved in the GPR analysis and validate the predictive accuracy of the improved EPRG model after the application of GPR. To facilitate the practical application of the improved EPRG model, a computer program with a graphic user interface (GUI) is further developed to compute the burst capacity of pipelines containing dent-gouges by inputting key parameters such as the pipe geometry and material properties as well as sizes of the dent and gouge through a GUI. This research will improve the fitness-for-service assessment of pipelines containing dent-gouges.
