ANFIS approach to the scour depth prediction at a bridge abutment

Journal of Hydroinformatics

Volumn 12, Issue 4, 2010, Pages 474-485

  Muzzammil, Mohammad ^a  

^a ALIGARH MUSLIM UNIVERSITY   (India)

Author keywords

ANFlS and regression analysis; Bridge abutments; Local scour; Neural network

Indexed keywords

EID: 78049394051     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2010.004     Document Type: Article

References (39)

1. Altunkaynak, A., Özger, M. & Çakmakcl, M. 2004a Fuzzy logic modeling of the dissolved oxygen fluctuations in golden horn. Ecol. Modell. 189 (3-4), 436-446.
2. Altunkaynak, A., Özger, M. & Çakmakcl, M. 2004& Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resour. Manage. 19 (5), 641-654.
3. ASCE TASK Committee 2000a Artificial neural networks in hydrology II: hydrologie applications. J. Hydrol. Eng. 5 (2), 124-137.
4. ASCE TASK Committee 2000& Artificial neural networks in hydrology I: preliminary concepts. J. Hydrol. Eng. 5 (2), 115-123.
5. Azmathullah, H. M., Deo, M. C. & Deolalikar, P. B. 2005 Neural Network for estimation of scour downstream of a skew-jump bucket. J. Hydraulic Eng. 131 (10), 898-908.
6. Azmathullah, H. M., Deo, M. C. & Deolalikar, P. B. 2006 Estimation of scour below spillways using neural networks. J. Hydraulic Res. 44 (1), 61-69.
7. Azmathullah, H. M., Deo, M. C. & Deolalikar, P. B. 2008 Alternative neural networks to estimate the scour below spillways. Adv. Eng. Softw. 39, 689-698.
8. Barbhuya, A. K. & Dey, S. 2004 Local scour at abutments: a review. Sadhana 29 (5), 449-476.
9. Bardossy, A. & Disse, A. 1993 Fuzzy rule based models for infiltration. Water Resour. Res. 29 (2), 373-382.
10. Bateman, A., Fernandez, M. & Parker, G. 2005 Morphodynamic model to predict temporal evolution of local scour at bridge piers. Proceedings of the 4th IAHR Symposium on River, Coastal and Estuarine Morphodynamics (RCEM 2005), Urbana, Illinois, USA, 4-7 October 2005.
11. Bateni, S. M. & Jeng, D. S. 2007 Estimation of pile group scour using adaptive neuro-fuzzy approach. Ocean Eng. 34, 1344-1354.
12. Bateni, S. M., Jeng, D. S. & Melville, B. W. 2007a Baysian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers. Adv. Eng. Softw. 38, 102-111.
13. Bateni, S. M., Borghei, S. M. & Jeng, D. S. 20076 Neural network and neuro-fuzzy assessment for scour depth around bridge piers. Eng. Appl. Artif. Intell. 20, 401-414.
14. Cardoso, A. H. & Bettes, R. 1999 Effects of time and channel geometry on scour at bridge abutments. J. Hydraulic Eng. 125 (4), 388-399.
15. Choi, S. U. & Cheong, S. 2006 Prediction of local scour around bridge piers using artificial neural networks. J. Am. Water Resour. Assoc. 42 (2), 487-494.
16. Dey, S. & Barbhuya, A. K. 2004a Clear water scour at abutments. Proc. Inst. Civil Eng. Water Manage. 157, 77-97.
17. Dey, S. & Barbhuya, A. K. 2004b Clear water scour at abutments in thinly armored beds. J. Hydraulic Eng. 130 (7), 622-634.
18. He, J. & Valeo, C. 2009 Comparative study of ANNs versus parametric methods in rainfall frequency analysis. J. Hydrol. Eng. 14(2), 172-184.
19. Huber, F. 1991 Update: Bridge Scour. Civil Engineering. ASCE, New York.
20. Jang, J. S. R. 1993 ANFIS: adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern 23 (3), 665-684.
21. Jeng, D. S., Bateni, S. M. & Lockett, E. 2005 Neural Network assessment for scour depth around bridge piers. Research Report No R855, Department of Civil Engineering, University of Sydney. NSW 2006, Australia.
22. Johnson, P. A. & Ayyub, B. M. 1996 Modeling uncertainty in prediction of pier scour. J. Hydraulic Eng. 122 (2), 66-72.
23. Kambeker, A. R. & Deo, M. C. 2003 Estimation of group pile scour using neural networks. J. Appl. Ocean Res. 25, 225-234.
24. Kindler, J. 1992 Rationalizing water requirements with aid of fuzzy allocation model. J. Water Resour. Plan. Manage. 118 (3), 308-323.
25. Lim, S. Y. 1997 Equilibrium clear-water scour around an abutment. J. Hydraulic Eng. 123, 237-243.
26. Liriano, S. L. & Day, R. A. 2001 Prediction of scour depth at culvert outlets using neural networks. J. Hydroinformatics 3 (4), 231-238.
27. Melville, B. W. & Chiew, Y. M. 1999 Time scale for local scour at bridge piers. Hydraulic Eng. 125, 59-65.
28. Muzzammil, M. & Siddiqui, R. 2003 An artificial neural network model for scour prediction. In Advances in Civil Engineering, Prospective of Developing Countries II (ed. D. Parmar & P. Kumar), pp. 430-441. Allied Publishers Pvt. Limited.
29. Pongracz, R., Bogardi, I. & Duckstein, L. 1999 Application of fuzzy rule-based modeling technique to regional drought. J. Hydrol. 224 (3-4), 100-114.
30. Şen, Z. & Altunkaynak, A. 2004 Fuzzy awakening in rainfall-runoff modelling. Nord. Hydrol. 35 (1), 31-43.
31. Shrestha, B. P., Duckstein, L. & Stakhiv, E. Z. 1996 Fuzzy rule based modeling of reservoir operation. J. Water Resour. Plan. Manage. 122 (4), 262-269.
32. Simarro, G., Teixeira, L. & Cardoso, H. 2007 Flow intensity parameter in pier scour experiments. J. Hydraulic Eng. 133 (11), 1261-1264.
33. Sun, Q. & Cheng, M. 2005 Modeling of doubly salient permanent magnet motor based on ANFIS. Proceedings of the Eighth International Conference on Electrical Machines and Systems, ICEMS-I, 321-325.
34. Tiwari, P. M. & Ayyub, M. 2006 Design and analysis of TakagiSugeno fuzzy model based controller for nonlinear systems. Proc. National Conf. on Energy, Communication, and Computer Systems (NC-ECC 2006), Feb. 2-4.
35. Trent, R., Gagarin, N. & Rhodes, J. 1993 Estimating pier scour with artificial neural networks. In: Hydraulic Engineering, Shen, H. W., Su, S. T. & Wen, F. (eds). ASCE, New York, pp. 1043-1048.
36. Uyumaz, A., AltunKaynak, A. & Ozger, M. 2006 Fuzzy logic model for equilibrium scour downstream of a dam's vertical gates. J. Hydraulic Eng. 132 (10), 1069-1075.
37. Wu, X. & Lim, S. Y. 1993 Prediction of maximum scour depth at spur dikes with adaptive neural networks, neural networks and combinatorial. In Civil & Structural Engineering (ed. B. H. V. Topping & A. I. Khan), pp. 61-66. Civil-Comp Press Ltd.
38. Yanmaz, A. M. & Kose, O. 2007 Time-wise variation of scouring at bridge abutments. Sadhna 3, 199-213.
39. Zounemat-Kermani, M., Baheshti, A. A., Ataie-Ashtiani, B. & Sabbagh-Yazdi, S. R. 2009 Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Appl. Soft Comput. 9 (2), 746-755.