Modelling of dam behaviour based on neuro-fuzzy identification

Engineering Structures

Volumn 35, Issue , 2012, Pages 107-113

  Ranković, Vesna ^a   Grujović, Nenad ^a   Divac, Dejan ^b   Milivojević, Nikola ^b   Novaković, Aleksandar ^a  

^a UNIVERSITY OF KRAGUJEVAC   (Serbia)

^b Institute for the Development of Water Resources Jaroslav Cerni   (Serbia)

Author keywords

ANFIS; Arch dam; Dam behaviour; Identification; Radial displacement

Indexed keywords

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; ANFIS; BEHAVIOUR MODELLING; COMPLEX TASK; ENGINEERING STRUCTURES; EXPERIMENTAL DATA; IDENTIFICATION MODEL; NEURO-FUZZY; NON-PARAMETRIC MODEL; NONLINEAR FUNCTIONS; RADIAL DISPLACEMENTS; SOFT COMPUTING MODELS; STRUCTURAL BEHAVIOUR; TIME VARYING;

ARCH DAMS; FORECASTING; HYDROSTATIC PRESSURE; IDENTIFICATION (CONTROL SYSTEMS); SOFT COMPUTING; TIME VARYING SYSTEMS;

HYDRAULIC STRUCTURES;

ACCURACY ASSESSMENT; COMPARATIVE STUDY; DAM; DISPLACEMENT; FUZZY MATHEMATICS; HYDROSTATIC PRESSURE; IDENTIFICATION METHOD; NUMERICAL MODEL; PREDICTION; STRUCTURAL ANALYSIS; TEMPERATURE EFFECT;

EID: 84855453194     PISSN: 01410296     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engstruct.2011.11.011     Document Type: Article

References (32)

1. ICOLD. Methods of analysis for the prediction and the verification of dam behaviour. Tech. Rep. Swiss Committee on Dams; 2003.
2. Perner F., Obernhuber P. Analysis of arch dam deformations. Front Archit Civ Eng China 2010, 4(1):102-108.
3. Szostak-Chrzanowski A., Chrzanowski A., Massiéra M. Use of deformation monitoring results in solving geomechanical problems - case studies. Eng Geol 2005, 79(1-2):3-12.
4. Mata J. Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 2011, 33(3):903-910.
5. De Sortis A., Paoliani P. Statistical analysis and structural identification in concrete dam monitoring. Eng Struct 2007, 29(1):110-120.
6. Fedele R., Maier G., Miller B. Identification of elastic stiffness and local stresses in concrete dams by in situ tests and neural networks. Struct Infrastruct Eng 2005, 1(3):165-180.
7. Ardito R., Bartalotta P., Ceriani L., Maier G. Diagnostic inverse analysis of concrete dams with statical excitation. J Mech Behav Mater 2004, 15(6):381-389.
8. Cao M., Qiao P., Ren Q. Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures. Neural Comput Appl 2009, 18(7):821-832.
9. Bayrak T. Modeling the relationship between water level and vertical displacements on the Yamula Dam, Turkey. Nat Hazards Earth Syst Sci 2007, 7(2):289-297.
10. Karimi I., Khaji N., Ahmadi M.T., Mirzayee M. System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach. Eng Struct 2010, 32(11):3583-3591.
11. Gülal E., Erdoĝan H., Aykut N.O., Erkaya H. The use of SISO ARX models in the determination of the damage to dams. Int J Dam Mech 2010, 10.1177/1056789510369333.
12. Chen C.H. Structural identification from field measurement data using a neural network. Smart Mater Struct 2005, 14(3):S104-S115.
13. Gupta P., Sinha N.K. An improved approach for nonlinear system identification using neural networks. J Franklin Inst 1999, 336(4):721-734.
14. Yazdizadeh A., Khorasani K. Adaptive time delay neural network structures for nonlinear system identification. Neurocomputing 2002, 47(1-4):207-240.
15. Yu W., Li X. Some new results on system identification with dynamic neural networks. IEEE Trans Neural Netw 2001, 12(2):412-417.
16. Gao Y., Er M.J. NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets Syst 2005, 150(2):331-350.
17. Yu W. Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms. Inform Sci 2004, 158:131-147.
18. Abdelazim T., Malik O.P. Identification of nonlinear systems by Takagi-Sugeno fuzzy logic gray box modeling for real-time control. Control Eng Pract 2005, 13(12):1489-1498.
19. Banakar A., Fazle Azeem M. Parameter identification of TSK neuro-fuzzy models. Fuzzy Sets Syst 2011, 10.1016/j.fss.2011.05.003.
20. Babuška R., Verbruggen H. Neuro-fuzzy methods for nonlinear system identification. Ann Rev Control 2003, 27(1):73-85.
21. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993, 23(3):665-685.
22. Jang J.S.R., Sun C.T., Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence 1997, Prentice-Hall, NJ.
23. Hamidian D., Seyedpoor S.M. Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization. Appl Math Model 2010, 34(6):1574-1585.
24. Seyedpoor S.M., Salajegheh J., Salajegheh E., Golizadeh S. Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks. Eng Optimiz 2009, 41(5):473-493.
25. Demirkaya S. Deformation analysis of an arch dam using ANFIS. In: Reiterer A, Egly U, Heinert M, Riedel B, editors. Proceedings of the second international workshop on application of artificial intelligence and innovations in engineering geodesy. Braunschweig, Germany; 2010. p. 21-31.
26. Sjöberg J., Zhang Q., Ljung L., Benveniste A., Deylon B., Glorennec P., et al. Nonlinear black-box modeling in system identification: a unified overview. Automatica 1995, 31(12):1691-1724.
27. Zeng X.J., Singh M.G. Approximation theory for fuzzy systems - SISO case. IEEE Trans Fuzzy Syst 1994, 20(2):162-176.
28. Kosko B. Fuzzy systems as universal approximator. IEEE Trans Comput 1994, 43(11):1329-1333.
29. Evsukoff A., Branco A.C.S., Galichet S. Structure identification and parameter optimization for non-linear fuzzy modelling. Fuzzy Sets Syst 2002, 132(2):173-188.
30. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1985, 15(1):116-132.
31. Sugeno M., Kang G.T. Structure identification of fuzzy model. Fuzzy Sets Syst 1988, 28(1):15-33.
32. Nelwamondo FV., Golding D., Marwala T.A. dynamic programming approach to missing data estimation using neural networks. Inform Sci 2009, 10.1016/j.ins.2009.10.008.