Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 3 SI
Page No. 3131 - 3138

A Hybrid Prediction Model for Pipeline Corrosion Using Artificial Neural Network with Particle Swarm Optimization

Authors : Lee Kien Ee and Izzatdin Abdul Aziz

References

Anonymous, 2016. Pipeline failure causes. U.S. Department of Transportation's Research and Special Programs Administration, Washington, USA. http://www.corrosion-doctors.org/Pipeline/Pipeline-failures.htm

Black, J. and R. Baldwin, 2012. When risk-based regulation aims low: A strategic framework. Regul. Governance, 6: 131-148.
CrossRef  |  Direct Link  |  

Demma, A., P. Cawley, M. Lowe, A.G. Roosenbrand and B. Pavlakovic, 2004. The reflection of guided waves from notches in pipes: A guide for interpreting corrosion measurements. NDT. E. Intl., 37: 167-180.
Direct Link  |  

Hassan, R., B. Cohanim and O. Weck, 2005. A comparison of particle swarm optimization and the genetic algorithm. Proceedings of the 1st AIAA Conference on Multidisciplinary Design Optimization Specialist, April 18-21, 2005, AIAA, Austin, Texas, pp: 18-21.

Hirao, M. and H. Ogi, 1999. An SH-wave EMAT technique for gas pipeline inspection. NDT. E. Intl., 32: 127-132.
Direct Link  |  

ILLC., 2016. Damage mechanisms. Inspectioneering, LLC., Ross Township, Pennsylvania. https://inspectioneering.com/topics

Kachitvichyanukul, V., 2012. Comparison of three evolutionary algorithms: GA, PSO and DE. Ind. Eng. Manage. Syst., 11: 215-223.
CrossRef  |  Direct Link  |  

Koehn, P., 1994. Combining genetic algorithms and neural networks: The encoding problem. Master Thesis, The University of Tennessee, Knoxville, Tennessee.

Krautkramer, J. and H. Krautkramer, 2013. Ultrasonic Testing of Materials. Springer, Berlin, Germany,.

Lowe, M.J.S., D.N. Alleyne and P. Cawley, 1998. Defect detection in pipes using guided waves. Ultrasonics, 36: 147-154.
CrossRef  |  Direct Link  |  

Ossai, C., 2012. Advances in asset management techniques: An overview of corrosion mechanisms and mitigation strategies for oil and gas pipelines. Intl. Scholarly Res. Netw., 2012: 1-10.
CrossRef  |  Direct Link  |  

Papavinasam, S., 2013. Corrosion Control in the Oil and Gas Industry. Elsevier, Amsterdam, Netherlands, ISBN:9780123973061, Pages: 1020.

Reber, K., M. Beller, H. Willems and O.A. Barbian, 2002. A new generation of ultrasonic in-line inspection tools for detecting, sizing and locating metal loss and cracks in transmission pipelines. Proceedings of the IEEE Symposium on Ultrasonics Vol. 1, October 8-11, 2002, IEEE, Munich, Germany, ISBN:0-7803-7582-3, pp: 665-671.

Ren, C., W. Qiao and X. Tian, 2012. Natural Gas Pipeline Corrosion Rate Prediction Model Based on BP Neural Network. In: Fuzzy Engineering and Operations Research, Cao, B.Y. and X.J. Xie (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-28591-2, pp: 449-455.

Rose, J.L., 2004. Ultrasonic guided waves in structural health monitoring. Key Eng. Mater., 273: 14-21.
CrossRef  |  Direct Link  |  

Singh, M. and T. Markeset, 2009. A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework. Eng. Fail. Anal., 16: 2098-2113.
Direct Link  |  

Sinha, S.K. and M.D. Pandey, 2002. Probabilistic neural network for reliability assessment of oil and gas pipelines. Comput. Aided Civ. Infrastruct. Eng., 17: 320-329.
CrossRef  |  Direct Link  |  

Supriyatman, D., K.A. Sidarto and R. Suratman, 2012. Artificial neural networks for corrosion rate prediction in gas pipelines. Proceedings of the International SPE Asia Conference on Pacific Oil and Gas and Exhibition, October 22-24, 2012, SPE Publisher, Perth, Australia, pp: 1-9.

Tong, H.C., 2015. A corrosion prediction model for subsea oil pipeline using enhanced associative classification. Proceedings of the International Conference on Computer and Information Sciences, Jun 28-July 01, 2015, IEEE, Las Vegas, Nevada, pp: 175-181.

USDT., 2016. Pipeline and hazardous materials safety administration. U.S. Department of Transportation, Washington, USA. https://www.phmsa.dot.gov/pipeline/library/data-stats/pipelineincidenttrends

Veiga, J., A. Carvalho, I.D. Silva and J. Rebello, 2005. The use of artificial neural network in the classification of pulse echo and TOFD ultrasonic signals. J. Braz. Soc. Mech. Sci. Eng., 27: 394-398.
Direct Link  |  

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