Failure pressure prediction of pipeline with single corrosion defect using artificial neural network
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 grade...
| Main Authors: | Chin, K.T., Arumugam, T., Karuppanan, S., Ovinis, M. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29456 / |
| Published: |
Transneft Media, LLC
2021
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112545678&doi=10.28999%2f2541-9595-2021-11-2-166-171&partnerID=40&md5=d8585634ed98af8f852810172a547412 http://eprints.utp.edu.my/29456/ |
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utp-eprints.294562022-03-25T02:07:09Z Failure pressure prediction of pipeline with single corrosion defect using artificial neural network Chin, K.T. Arumugam, T. Karuppanan, S. Ovinis, M. 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 (R2) 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. © 2021, Transneft Media, LLC. All rights reserved. Transneft Media, LLC 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112545678&doi=10.28999%2f2541-9595-2021-11-2-166-171&partnerID=40&md5=d8585634ed98af8f852810172a547412 Chin, K.T. and Arumugam, T. and Karuppanan, S. and Ovinis, M. (2021) Failure pressure prediction of pipeline with single corrosion defect using artificial neural network. Science and Technologies: Oil and Oil Products Pipeline Transportation, 11 (2). pp. 166-171. http://eprints.utp.edu.my/29456/ |
| institution |
Universiti Teknologi Petronas |
| collection |
UTP Institutional Repository |
| description |
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 (R2) 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. © 2021, Transneft Media, LLC. All rights reserved. |
| format |
Article |
| author |
Chin, K.T. Arumugam, T. Karuppanan, S. Ovinis, M. |
| spellingShingle |
Chin, K.T. Arumugam, T. Karuppanan, S. Ovinis, M. Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| author_sort |
Chin, K.T. |
| title |
Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| title_short |
Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| title_full |
Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| title_fullStr |
Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| title_full_unstemmed |
Failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| title_sort |
failure pressure prediction of pipeline with single corrosion defect using artificial neural network |
| publisher |
Transneft Media, LLC |
| publishDate |
2021 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112545678&doi=10.28999%2f2541-9595-2021-11-2-166-171&partnerID=40&md5=d8585634ed98af8f852810172a547412 http://eprints.utp.edu.my/29456/ |
| _version_ |
1741197244474851328 |
| score |
11.62408 |