Failure pressure prediction of pipeline with single corrosion defect using artificial neural network



Published Mar 31, 2020
Kiu Toh Chin Thibankumar Arumugam Saravanan Karuppanan Mark Ovinis


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.

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Chin KT, Arumugam T, Karuppanan S, Ovinis M. Failure pressure prediction of pipeline with single corrosion defect using artificial neural network. PST [Internet]. 2020Mar.31 [cited 2020Jul.8];4(1(3):10-7. Available from:


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pipe corrosion, single defect, internal pressure, artificial neural network

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Original Work