This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of 71 sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error (within 15%). Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

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.

Due to their exceptional structural integrity, steel pipelines are the main component for oil and gas transmission. However, these pipelines are often affected by corrosion, despite corrosion protection, because of harsh working conditions. In addition to corrosion defects, pipelines are often subjected to multiple external loads. The combination of corrosion defects and external loads can significantly reduce the failure pressure, resulting in various failure behaviors. This reduction in failure pressure is especially critical in pipe bends as they are the weakest link in a pipeline. This paper presents an overview of the failure behavior of corroded steel pipe components subjected to internal pressure and axial compressive stress.

Описываются разработка и применение искусственной нейронной сети (ИНС) для прогнозирования предельного давления трубопровода с точечным коррозионным дефектом, подверженного воздействию только внутреннего давления. Модель ИНС разработана на основе данных, полученных по результатам множественных полномасштабных испытаний на разрыв труб API 5L (класс от X42 до X100). Качество работы модели ИНС проверено в сравнении с данными для обучения, получен коэффициент детерминации R = 0,99. Модель дополнительно протестирована с учетом данных о предельном давлении корродированных труб API 5L X52 и X80. Установлено, что разработанная модель ИНС позволяет прогнозировать предельное давление с приемлемой погрешностью. С использованием данной модели проведена оценка влияния длины и глубины коррозионных дефектов на предельное давление. Выявлено, что глубина коррозии является более значимым фактором разрушения корродированного трубопровода. This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination ( R ) of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error. Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.