[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: ::
Back to the articles list Back to browse issues page
Optimization of Adaptive Neuro-Fuzzy Inference System using Differential Evolution Algorithm for Scour Prediction around Submerged Pipes
Ali Reza Mahmodian, Behrouz Yaghoubi , Fariborz Yosevfand
Department of Water Engineering, College of Agriculture, Islamic Azad University, Kermanshah Branch, Kermanshah
Abstract:   (235 Views)
Nowadays, a huge amount of natural resources such as gases and oil are exploited from offshore oil fields and transported by pipes located at seabed. The pipelines are exposed to waves and currents and scour may occur around them. Subsequently, stability of the pipes can be threatened, so estimation and simulation of scouring around the pipes are quite vital. In this study, a hybrid method for simulating the scour depth in the vicinity of submerged pipes was developed. In other words, the adaptive neuro-fuzzy inference system (ANFIS) and the differential algorithm were combined with each other to simulate the scour depth. In general, ANFIS is an artificial neural network acts based on the Takagi-Sugeno inference system. This model is a set of if-then rules which is able to approximate non-linear functions. In addition, the differential algorithm is a powerful evolutionary algorithm among optimization algorithms which have many applications in scientific fields. In this study, the Monte-Carlo simulation was employed for examining the ability of numerical models. To validate the modeling results, the k-fold cross validation approach was also utilized with k=6. Then, the parameters affecting the scour depth were detected and six ANFIS and hybrid models were developed for scour estimation. After that, the results of the mentioned models were examined and this analysis showed that the superior model predicts scour values in terms of all input parameters. This model has reasonable accuracy. For example, the values of R and RMSE for this model were calculated 0.974 and 0.079, respectively. Furthermore, the analysis of the modeling results indicated that the ratio of the pipe distance from the sedimentary bed to the pipe diameter (e/D) was identified as the most effective parameter.
Keywords: Scouring, ANFIS, Differential Evolution Algorithm, Submerged Pipes, Hybrid Model
Full-Text [PDF 1033 kb]   (44 Downloads)    
Type of Study: Research | Subject: Offshore Engineering
Received: 2018/07/7 | Accepted: 2019/04/27
References
1. Hansen, E.A., Fredsoe, J. and Mao, Y., (1986), Two-directional scour below pipelines, Fifth Internat. Symp. on Offshore Mech. and Arctic Energy, American Society of Mechanical Engineering, Vol. 3, p.670.
2. Sumer, B.M., Mao, Y. and Freds√łe, J., (1988), Interaction between vibrating pipe and erodible bed, Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 114(1), p.81-92. [DOI:10.1061/(ASCE)0733-950X(1988)114:1(81)]
3. Mao, Y., (1988), Seabed scour under pipelines, In OMAE 1988 Houston, Proc. 7th Int. Conf. on Offshore Mechanics and Arctic Engineering, Am. Soc. Civ. Engineers, Houston, TX, 7-12.
4. Chiew, Y.M., (1993), Effect of spoilers on wave-induced scour at submarine pipelines, Journal of waterway, port, coastal, and ocean engineering, Vol. 119(4), p.417-428. [DOI:10.1061/(ASCE)0733-950X(1993)119:4(417)]
5. Moncada-M, A.T. and Aguirre-Pe, J., (1999), Scour below pipeline in river crossings, Journal of Hydraulic Engineering, Vol. 125(9), p.953-958. [DOI:10.1061/(ASCE)0733-9429(1999)125:9(953)]
6. Teh, T.C., Palmer, A.C. and Damgaard, J.S., (2003), Experimental study of marine pipelines on unstable and liquefied seabed, Coastal Engineering, Vol. 50(1), p.1-17. [DOI:10.1016/S0378-3839(03)00066-8]
7. Dey, S. and Singh, N.P., (2008), Clear-water scour below underwater pipelines under steady flow, Journal of hydraulic engineering, Vol. 134(5), p.588-600. [DOI:10.1061/(ASCE)0733-9429(2008)134:5(588)]
8. Wu, Y. and Chiew, Y.M., (2012), Three-dimensional scour at submarine pipelines, Journal of Hydraulic Engineering, Vol. 138(9), p.788-795. [DOI:10.1061/(ASCE)HY.1943-7900.0000583]
9. Yang, L., Guo, Y., Shi, B., Kuang, C., Xu, W. and Cao, S., (2012), Study of scour around submarine pipeline with a rubber plate or rigid spoiler in wave conditions, Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 138(6), p.484-490. [DOI:10.1061/(ASCE)WW.1943-5460.0000150]
10. Luan, Y., Liang, D. and Rana, R., (2015), Scour depth beneath a pipeline undergoing forced vibration, Theoretical and Applied Mechanics Letters, Vol. 5(2), p.97-100. [DOI:10.1016/j.taml.2015.03.002]
11. Etemad-Shahidi, A., Yasa, R. and Kazeminezhad, M.H., (2011), Prediction of wave-induced scour depth under submarine pipelines using machine learning approach. Applied Ocean Research, Vol. 33(1), p.54-59. [DOI:10.1016/j.apor.2010.11.002]
12. Najafzadeh, M., Barani, G.A. and Hessami Kermani, M.R., (2014), Estimation of pipeline scour due to waves by GMDH. Journal of Pipeline Systems Engineering and Practice, Vol. 5(3), p.06014002. [DOI:10.1061/(ASCE)PS.1949-1204.0000171]
13. Azimi, H., Shabanlou, S., Ebtehaj, I., Bonakdari, H. and Kardar, S., (2017), Combination of computational fluid dynamics, adaptive neuro-fuzzy inference system, and genetic algorithm for predicting discharge coefficient of rectangular side orifices. Journal of Irrigation and Drainage Engineering, Vol. 143(7), p.04017015. [DOI:10.1061/(ASCE)IR.1943-4774.0001190]
14. Khoshbin, F., Bonakdari, H., Ashraf Talesh, S. H., Ebtehaj, I., Zaji, A. H. and Azimi, H., (2016), Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs, Engineering Optimization, Vol. 48(6), p.933-948. [DOI:10.1080/0305215X.2015.1071807]
15. Ebtehaj, I., Sattar, A., Bonakdari, H. and Zaji, A. H., (2017), Prediction of scour depth around bridge piers using self-adaptive extreme learning machine, Journal of Hydroinformatics, Vol. 19(2), p.207-224. [DOI:10.2166/hydro.2016.025]
16. Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S. H. A., Michelson, D. G. and Jamali, A., (2017), Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition, Fuzzy Sets and Systems, Vol. 319, p.50-69. [DOI:10.1016/j.fss.2016.10.010]
17. Azimi, H., Bonakdari, H., Ebtehaj, I. and Michelson, D. G, (2018), A combined adaptive neuro-fuzzy inference system-firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed, Neural Computing and Applications, Vol. 29(6), p.249-258. [DOI:10.1007/s00521-016-2560-9]
18. Jang, J.S., (1993), ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, Vol. 23(3), p.665-685. [DOI:10.1109/21.256541]
19. Storm, R. and Price, K., (1995), Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI Technical Report.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML     Print



Back to the articles list Back to browse issues page
International Journal of Coastal and Offshore Engineering International Journal of Coastal and Offshore Engineering
Persian site map - English site map - Created in 0.05 seconds with 32 queries by YEKTAWEB 3920