Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members

Engineering Structures

Volumn 32, Issue 6, 2010, Pages 1723-1734

  Mashrei, Mohammed A ^a   Abdulrazzaq, Nabeel ^b   Abdalla, Turki Y ^b   Rahman, M S ^c  

^a UNIVERSITY OF THI QAR   (Iraq)

^b UNIVERSITY OF BASRAH   (Iraq)

^c North Carolina State University   (United States)

Author keywords

Adaptive neuro fuzzy inference system; Ferrocement; Moment; Neural networks

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; BACK-PROPAGATION NEURAL NETWORKS; FERROCEMENT; INPUT VARIABLES; MOMENT CAPACITY; NEURAL NETWORKS MODEL; PARAMETRIC STUDY; PREDICTIVE TOOLS; WIRE MESHES;

COMPRESSIVE STRENGTH; CONCRETE BRIDGES; FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; REINFORCED CONCRETE; TENSILE STRENGTH;

FUZZY SYSTEMS;

ARTIFICIAL NEURAL NETWORK; COMPRESSIVE STRENGTH; MODELING; MORTAR; TENSILE STRENGTH;

EID: 77952547031     PISSN: 01410296     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engstruct.2010.02.024     Document Type: Article

References (47)

1. Naaman A.E. Ferrocement and laminated cementitious composites, book 2000.
2. Hanna M.A., Ural D., Saygili G. Evaluation of liquefaction potential of soil deposits using artificial neural networks. J Comput Aided Eng Softw 2007, 24(1):5-16.
3. Padmini D., Iamparuthi K., Sudheer K.P. Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. J Comput Geotech 2008, 35:33-46.
4. Gershenson C. Artificial neural networks for beginners. In: Cognitive and computing sciences. University of Sussex.
5. Flood I., Kartam N. Neural networks in civil engineering, principle and understanding. ASCE J Comput Civil Eng 1994, 8(2):131-148.
6. Adeli H., Yeh C. Perception learning in engineering design. Microcomput Civil Eng 1989, 4(4):247-256.
7. Feng M.Q., Bahng E.Y. Damage assessment of jacketed RC columns using vibration tests. J Struct Eng 1999, 125(3):265-271.
8. Chen SS, Shah K. Neural networks in dynamic analysis of bridges. In Proc., 8th conf. computing in civil engineering. New York: ASCE; 1992. p. 1058-65.
9. Feng M.Q., Kim J.M. Identification of a dynamic system using ambient vibration measurements. J Appl Mech 1998, 65(2):1010-1023.
10. Adeli H., Park H.S. Comput Struct 1995, 57(3):383-390.
11. Chen H.M., Tsai K.H., Qi G.Z., Yang C.S., Amini F. Neural network for structure control. J Comput Civil Eng 1995, 9(2):168-176.
12. Oh J.W., Lee I.W., Kim J.T., Lee G.W. Application of neural networks for proportioning of concrete mixes. ACI Mater J 1999, 96(1):61-67.
13. Sanad A., Saka M.P. Prediction of ultimate shear strength of reinforced concrete deep beams using neural networks. J Struct Eng 2001, 127(7):818-828.
14. Cladera A., Mar A.R. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: beams without stirrups. J Eng Struct 2004, 26:917-926.
15. Cladera A., Mar A.R. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups. J Eng Struct 2004, 26:927-936.
16. Seleemah A.A. A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement. Can J Civil Eng 2005, 32:644-657.
17. Abdallaa J.A., Elsanosib A., Abdelwahab A. Modeling and simulation of shear resistance of R/C beams using artificial neural network. J Franklin Inst 2007, 344:741-756.
18. Jang S.R. Adaptive network-based fuzzy inference system. IEEE 1993, 23(3):665-685.
19. Qian H., Xia B., Li S.Z., Wang F. Fuzzy neural network modeling of material properties. J Mater Process Tech 2002, 122:196-200.
20. Akbuluta S., Samet H., Pamuk S. Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system. J Soil Dyn Earthq Eng 2004, 24:805-814.
21. I'nan G., Göktepe A.B., Ramyar A. Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology. Build Environ 2007, 42:1264-1269.
22. Tesfamariam S., Najjaran H. Adaptive network-fuzzy inferencing to estimate concrete strength using mix design. J Mater Civil Eng 2007, 19(7):550-560.
23. Logan D., Shah S.P. Moment capacity and cracking behavior of ferrocement in flexure. ACI J 1973, 70(12):799-804.
24. Alwash A. S. Flexural characteristics of ferrocement. Ms.c. thesis. Iraq: University of Baghdad; 1974.
25. Balaguru P.N., Naaman A.E., Shah S.P. Analysis and behavior of ferrocement in flexture. ASCE 1977, 1937-1951.
26. Paramasivam P., Mansur M.S., Ong K.C. Flexural behavior of light-weight ferrocement slabs. J Ferrocement 1985, 15(1):25-33.
27. Mansur M.A., Paramasivam P. Cracking behavior and ultimate strength of ferrocement in flexure. J Ferrocement 1986, 16(4):405-415.
28. Mansur M.A. Ultimate strength design of ferrocement in flexure. J Ferrocement 1988, 18(4):385-395.
29. Paramasivam P., Ravindrajah R.S. Effect of arrangements of reinforcements on mechanical properties of ferrocement. ACI Struct J 1988, 3-11.
30. Desayi P., Reddy V. Strength of lightweight ferrocement in flexure. J Cement Concr Composites 1991, 13:13-20.
31. Naaman A.E., Homeric J.R. Flexural design of ferrocement computerized evaluation and design aids. J Ferrocement 1986, 16(2):101-116.
32. Waszczyszyn Z, Pabisek E, Mucha G. Hybrid neural network/computational program to the analysis of elastic-plastic structures. In: Neural networks in Mechanics of Structures and Materials. Udine, Italy; 1998. p. 19-23.
33. Topcu I.B., Saridemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 2007.
34. Baughman D.R., Liu Y.A. Neural network in bioprocessing and chemical engineering 1995, Academic Press, San Diego (CA).
35. Garson G.D. Interpreting neural-network connection weights. AI Expert 1991, 6(7):47-51.
36. Zadeh L.A. Fuzzy sets. Inf Control 1965.
37. Fuzzy logic toolbox user's guide for use with MATLAB 2004.
38. Zaho J., Bose K.B. Evaluation of membership functions for fuzzy logic controlled induction motor drive. IEEE J 2002, 229-234.
39. Jang S.R., Sun T.C., Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence 1997, Prentice Hall, Inc.
40. Lin C.T., Lee C. Neural fuzzy systems-a neuro fuzzy synergism to intelligent systems 1996, Prentice Hall P T R, Upper Saddle River (NJ).
41. Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Studies 1975, 7(1):1-13.
42. Sugeno M. Industrial applications of fuzzy control 1985, Elsevier Science Pub., Co.
43. Hamidian D., Seyedpoor M.S. Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization. J Appl. Math. Modelling 2009.
44. Chiu S. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 1994, 2(3).
45. Joni S., Jankovi T., Gaji V., Popovi D. Three machine learning techniques for automatic determination of rules to control locomotion. J IEEE 1999, 46(3):300-310.
46. Lakhankar T., Ghedira H., Temimi M., Sengupta M., Reza K., Blake R. Non-parametric methods for soil moisture retrieval from. J Remote Sensing 2009, 3-21.
47. Hammouda K., Karray F. A comparative study of data clustering techniques 2001, University of Waterloo, Ontario (Canada).