Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

Applied Ocean Research

Volumn 33, Issue 1, 2011, Pages 54-59

  Etemad Shahidi, A ^a   Yasa, R ^a   Kazeminezhad, M H ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Clear water; Initial gap; Live bed; M5' model tree; Scour depth

Indexed keywords

CLEAR WATER; INITIAL GAP; LIVE BED; M5' MODEL TREE; SCOUR DEPTH;

FORECASTING; LEARNING SYSTEMS; NEURAL NETWORKS; PIPELINES; SCOUR; SOFT COMPUTING; SUBMARINE PIPELINES; SUBMARINES;

BRIDGE PIERS;

ARTIFICIAL NEURAL NETWORK; NUMERICAL METHOD; NUMERICAL MODEL; PREDICTION; SCOUR; SUBMARINE PIPELINE;

EID: 79951576476     PISSN: 01411187     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apor.2010.11.002     Document Type: Article

References (33)

1. Zhao Z.H., Fernando H.J.S. Numerical simulation of scour around pipelines using an Euler-Euler coupled two-phase model. Environmental Fluid Mechanics 2007, 7:121-142.
2. Sumer B.M., Fredsøe J. The mechanics of scour in the marine environment 2002, World Scientific, Singapore.
3. Chen C, Zhang J. A review on scour modeling below pipelines. In: Proceedings of pipelines 2009: infrastructures hidden assets. ASCE. 2009. p. 1019-28.
4. Mao Y. The interaction between a pipeline and an erodible bed, Institute of Hydrodynamics and Hydraulic Engineering. Lyngby (Denmark): Technical University of Denmark; 1986.
5. Sumer B.M., Truelsen C., Sichmann T., Fredsøe J. Onset of scour below pipelines and self-burial. Coastal Engineering 2001, 42:313-335.
6. Lucassen RJ. Scour underneath submarine pipelines. MATs rep. PL-4 2A. Marine tech. res., The Netherlands. 1984.
7. Sumer B.M., Fredsøe J. Scour below pipelines in waves. Journal of Waterway, Port, Coastal, and Ocean Engineering ASCE 1990, 116:307-323.
8. Pu Q., Li K., Gao F.P. Scour of the seabed under a pipeline in oscillating flow. China Ocean Engineering 2001, 15:129-137.
9. Kazeminezhad M.H., Etemad-Shahidi A., Bakhtiary A.Y. An alternative approach for investigation of the wave-induced scour around pipelines. Journal of Hydroinformatics 2010, 12:51-65.
10. Liang D.F., Cheng L. Numerical model for wave-induced scour below a submarine pipeline. Journal of Waterway, Port, Coastal, and Ocean Engineering ASCE 2005, 131:193-202.
11. Kambekar A.R., Deo M.C. Estimation of pile group scour using neural networks. Applied Ocean Research 2003, 25:225-234.
12. Bateni S.M., Borghei S.M., Jeng D.S. Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence 2007, 20:401-414.
13. Bateni S.M., Jeng D.S., Melville B.W. Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers. Advances in Engineering Software 2007, 38:102-111.
14. Deo M.C., Jha A., Chaphekar A.S., Ravikant K. Neural networks for wave forecasting. Ocean Engineering 2001, 28:889-898.
15. Kazeminezhad M.H., Etemad-Shahidi A., Mousavi S.J. Application of fuzzy inference system in the prediction of wave parameters. Ocean Engineering 2005, 32:1709-1725.
16. Zhang Z.X., Li C.W., Qi Y.Q., Li Y.S. Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting. Journal of Hydroinformatics 2006, 8:65-76.
17. Mahjoobi J., Etemad-Shahidi A., Kazeminezhad M.H. Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research 2008, 30:28-36.
18. Ruessink B.G. Calibration of nearshore process models-application of a hybrid genetic algorithm. Journal of Hydroinformatics 2005, 7:135-149.
19. Bonakdar L., Etemad-Shahidi A. Predicting wave run-up on rubble-mound structures using M5 model tree. Ocean Enginering 2010, 10.1016/j.oceaneng.2010.09.015.
20. Bhattacharya B., Price R.K., Solomatine D.P. Machine learning approach to modeling sediment transport. Journal of Hydraulic Engineering, ASCE 2007, 133:440-450.
21. Etemad-Shahidi A., Bonakdar L. Design of rubble-mound breakwaters using M5' machine learning method. Applied Ocean Research 2009, 31:197-201.
22. Etemad-Shahidi A., Mahjoobi J. Comparison between M5' model tree and neural networks for prediction of significant wave height in lake superior. Ocean Engineering 2009, 36:1175-1181.
23. Sakhare S., Deo M.C. Derivation of wave spectrum using data driven methods. Marine Structures 2009, 22:594-609.
24. Chiew Y.M. Mechanics of local scour around submarine pipelines. Journal of Hydraulic Engineering, ASCE 1990, 116:515-529.
25. Cevik E., Yuksel Y. Scour under submarine pipelines in waves in shoaling conditions. Journal of Waterway, Port, Coastal, and Ocean Engineering, ASCE 1999, 125:9-19.
26. Mousavi M.E., Bakhtiary A.Y., Enshaei N. The equivalent depth of wave-induced scour around offshore pipelines. Journal of Offshore Mechanics and Arctic Engineering, Transactions of the ASME 2009, 131.
27. Xu J., Li G., Dong P., Shi J. Bed form evolution around a submarine pipeline and its effects on wave-induced forces under regular waves. Ocean Engineering 2010, 37:304-313.
28. Quinlan J.R. Learning with continuous classes. Proceedings of AI'92 1992, 343-348. World Scientific. Adams, Sterling (Eds.).
29. Wang Y, Witten IH. Induction of model trees for predicting continuous lasses. In: Proceedings of the poster papers of the European conference on machine learning. Prague: University of Economics, Faculty of Informatics and Statistics; 1997.
30. Weka, Waikato environment for knowledge analysis software. New Zealand: University of Waikato; 1999-2009.
31. Soulsby R. Dynamics of marine sands: a manual for practical applications 1997, Thomad Telford, London.
32. Witten I.H., Frank E. Data mining: practical machine learning tools and techniques 2005, Morgan Kaufmann, San Francisco (CA).
33. Yang B., Gao F.P., Wu Y.X. Experimental study on local scour of sandy seabed under submarine pipeline in unidirectional currents. Gongcheng Lixue/Engineering Mechanics 2008, 25:206-210.