Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete

Construction and Building Materials

Volumn 25, Issue 8, 2011, Pages 3486-3494

  Gencel, Osman ^a   Kocabas, Fikret ^a   Gok, Mustafa Sabri ^a   Koksal, Fuat ^b  

^a BARTIN UNIVERSITY   (Turkey)

^b BOZOK UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Concrete; General linear model; Hematite; Wear

Indexed keywords

ABRASIVE WEARS; ARTIFICIAL NEURAL NETWORK; CEMENT CONTENT; COMPARISON RESULT; DATA SETS; DETERMINATION COEFFICIENTS; EVALUATION CRITERIA; EXPERIMENTAL STUDIES; GENERAL LINEAR MODEL; GENERAL LINEAR MODELS; ROOT MEAN SQUARE ERRORS; TEST SETS; TRAINING SETS; WEAR;

ABRASION; ABRASIVES; CEMENTS; HEMATITE; IRON ORES; MATHEMATICAL MODELS;

NEURAL NETWORKS;

EID: 79955623437     PISSN: 09500618     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conbuildmat.2011.03.040     Document Type: Article

References (56)

1. H. Li, M.H. Zhang, and J.P. Ou Abrasion resistance of concrete containing nano-particles for pavement Wear 260 2006 1262 1266 (Pubitemid 43765990)
2. T. Sebok, and O. Stranel Wear resistance of polymer-impregnated mortars and concrete Cem Concr Res 34 2004 1853 1858
3. W. Brostow, and H.E. Hagg Lobland Predicting wear from mechanical properties of thermoplastic polymers Polym Eng Sci 48 2008 1982 1985
4. L.A. Gyurova, P.M. Justel, and A.K. Schlarb Modeling the sliding wear and friction properties of polyphenylene sulfide composites using artificial neural networks Wear 268 2010 708 714
5. T.R. Naik, S.S. Singh, and M.M. Hossain Abrasion resistance of concrete as influenced by inclusion of fly ash Cem Concr Res 24 2 1994 303 312
6. C.D. Atis High volume fly ash abrasion resistant concrete J Mater Civil Eng 14 3 2002 274 277 (Pubitemid 34641827)
7. Z. Zhang, and K. Friedrich Artificial neural networks applied to polymer composites: a review Compos Sci Technol 63 2003 2029 2044 (Pubitemid 37106179)
8. Z. Jiang, Z. Zhang, and K. Friedrich Prediction on wear properties of polymer composites with artificial neural networks Compos Sci Technol 67 2007 168 176 (Pubitemid 44872662)
9. Z. Zhang, K. Friedrich, and K. Velten Prediction on tribological properties of short fibre composites using artificial neural networks Wear 252 2002 668 675 (Pubitemid 34554001)
10. F. Altun, O. Kisi, and K. Aydin Predicting the compressive strength of steel fiber added lightweight concrete using neural network Comput Mater Sci 42 2008 259 265 (Pubitemid 351335260)
11. O. Kisi Suspended sediment estimation using neuro-fuzzy and neural network approaches Hydrol Sci J 50 2007 683 696
12. I.C. Yeh Design of high-performance concrete mixture using neural networks and nonlinear programming J Comput Civil Eng 13 1999 36
13. J.W. Oh Application of neural networks for proportioning of concrete mixes ACI Mater J 96 1999 61
14. T. Ji A mortar mix proportion design algorithm based on artificial neural networks Comput Concr 3 2006 357
15. T. Ji, T. Lin, and X. Lin A concrete mix proportion design algorithm based on artificial neural networks Cem Concr Res 36 2006 1399 1408 (Pubitemid 44226359)
16. A. Oztas, M. Pala, E. Ozbay, E. Kanca, N. Caglar, and M.A. Bhatti Predicting the compressive strength and slump of high strength concrete using neural network Constr Build Mater 20 9 2006 769 775 (Pubitemid 43795504)
17. I.C. Yeh Modeling slump flow of concrete using second-order regressions and artificial neural networks Cem Concr Compos 29 2007 474 480
18. I.C. Yeh Modeling slump of concrete with fly ash and superplasticizer Comput Concr 5 6 2008 559 572
19. I.C. Yeh Prediction of workability of concrete using design of experiments for mixtures Comput Concr 5 1 2008 1 20
20. F. Demir Prediction of elastic modulus of normal and high strength concrete by artificial neural networks Constr Build Mater 22 2008 1428 1435 (Pubitemid 351626557)
21. M. Bilgehan, and P. Turgut Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity Res Nondestruct Eval 21 2010 1 17
22. M. Bilgehan, and P. Turgut The use of neural networks in concrete compressive strength estimation Comput Concr 7 3 2010 271 283
23. S.C. Lee Prediction of concrete strength using artificial neural networks Eng Struct 25 2003 849 857 (Pubitemid 36558074)
24. A. Mukherjee, and S.N. Biswas Artificial neural networks in prediction of mechanical behavior of concrete at high temperature Nucl Eng Des 178 1997 1 11 (Pubitemid 127433768)
25. I.B. Topcu, and M. Saridemir Prediction of properties of waste AAC aggregate concrete using neural network Comput Mater Sci 41 2007 117 125 (Pubitemid 47576602)
26. H. Erdem Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural Networks Adv Eng Softw 41 2 2010 270 276
27. M.F. Kaplan Concrete radiation shielding 1989 Wiley New York
28. ASTM Standard C127, Standard test method for density, relative density (Specific Gravity), and absorption of coarse aggregate, ASTM Annual Book of ASTM standards, Pennsylvania, 2007.
29. ASTM Standard C128, Standard test method for density, relative density (Specific Gravity), and absorption of fine aggregate, ASTM Annual Book of ASTM standards, Pennsylvania, 2007.
30. ASTM Standard C29, Standard test method for bulk density (unit weight) and voids in aggregate, Annual Book of ASTM Standards, Pennsylvania, 2007.
31. O. Gencel, W. Brostow, C. Ozel, and M. Filiz Concretes containing hematite for use as shielding barriers Mater Sci - Medzg 16 3 2010 249 256
32. ASTM C511, Standard specification for mixing rooms, moist cabinets, moist rooms, and water storage tanks used in the testing of hydraulic cements and concretes, Annual Book of ASTM Standards, Pennsylvania, 2006.
33. ASTM C39, Standard test method for compressive strength of cylindrical concrete specimens, Annual Book of ASTM Standards, Pennsylvania, 2009.
34. TS 699, Natural building stones - Methods of inspection and laboratory testing, Turkish Standards Institution, Ankara, 2009 [in Turkish].
35. ASTM C779, Standard test method for abrasion resistance of horizontal concrete surfaces, Annual Book of ASTM Standards, Pennsylvania, 2000.
36. S. Yazici, and G. Inan An investigation on the wear resistance of high strength concretes Wear 260 2006 615 618 (Pubitemid 43276115)
37. M. Arslan The effects of permeable formworks with sucker liners on the physical properties of concrete surfaces Constr Build Mater 15 2001 149 156 (Pubitemid 32277494)
38. S. Haykin Neural networks: a comprehensive foundation 2nd ed. 1998 Prentice-Hall Upper Saddle River, New Jersey
39. H.K. Cigizoglu, and O. Kisi Flow prediction by three backpropagation techniques using k-fold partitioning of neural network training data Nordic Hydrol 36 1 2005 49 64 (Pubitemid 40620979)
40. M.T. Hagan, and M. Menhaj Training feedforward networks with the Marquardt algorithm IEEE Trans Neural Netw 5 6 1994 989 993
41. F. Kocabas, O. Kisi, and M. Ardiclioglu An artificial neural network model for prediction of critical submergence for an intake in a stratified fluid media Civil Eng Environ Syst 26 4 2009 367 375
42. MATLAB (ver 7.0). The MathWorks Inc., 3 Apple Hill Drive, Natick (MA).
43. More JJ. Lecture notes in mathematics. The Levenberg-Marquardt algorithm: implementation and theory. In: Watson GA, editor. Numerical analysis, vol. 630. Springer Verlag; 1997. p. 105-116.
44. O. Kisi, and H.K. Cigizoglu Comparison of different ANN techniques in river flow prediction Civil Eng Environ Syst 14 51 2007 211 231 (Pubitemid 47351487)
45. F. Kocabas, S. Unal, and B. Unal A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom Comput Fluids 37 2008 1040 1046
46. O. Gencel The application of artificial neural networks technique to estimate mass attenuation coefficient of shielding barrier Int J Phys Sci 4 12 2009 743 751
47. O. Kisi Stream flow forecasting using different artificial neural network algorithms J Hydrol Eng 12 5 2007 532 539 (Pubitemid 47307903)
48. SPSS Inc. SPSS base 12.0 user's guide. Chicago, IL: USA; 2003.
49. Y. Tasdemir, and E. Agar Investigation of the low temperature performances of polymer and fiber modified asphalt mixtures Indian J Eng Mater Sci 14 2007 151 157
50. F. Kocabas, and S. Unal Compared techniques for the critical submergence of an intake in water flow Adv Eng Softw 41 5 2010 802 809
51. W.C. Dawson, and R. Wilby An artificial neural network approach to rainfall-runoff modeling Hydrol Sci J 43 1 1998 47 66
52. O. Kisi Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural Networks Hydrol Proces 23 2 2008 213 223
53. O. Kisi, and M. Cobaner Modeling river stage-discharge relationships using different neural network computing techniques Clean-Soil-Air-Water 37 2 2009 160 169
54. H.K. Cigizoglu Estimation, forecasting and extrapolation of river flows by artificial neural networks Hydrol Sci J 48 3 2003 349 361
55. O.P. Modi, D.P. Mondal, B.K. Prasad, M. Singh, and H.K. Khaira Abrasive wear behavior of high carbon steel: effects of microstructure and experimental parameters and correlation with mechanical properties Mater Sci Eng A 343 2003 235 242 (Pubitemid 36036000)
56. Papenfus N. Applying concrete technology to abrasion resistance. In Proceeding of the 7th international conference on concrete block paving, 2003, Sun City, South Africa.