Prediction of elastic modulus of normal and high strength concrete by support vector machine

Construction and Building Materials

Volumn 24, Issue 8, 2010, Pages 1479-1485

  Yan, Kezhen ^a   Shi, Caijun ^a  

^a HUNAN UNIVERSITY   (China)

Author keywords

Compressive strength; Concrete; Elastic modulus; Support vector machine

Indexed keywords

CONCRETE ELASTIC MODULUS; EXPERIMENTAL DATA; HIGH STRENGTH CONCRETES; LEARNING THEORY; LOSS FUNCTIONS; PREDICTION MODEL; REGRESSION TECHNIQUES;

COMPRESSIVE STRENGTH; ELASTIC MODULI; FORECASTING; MATHEMATICAL MODELS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 77951208514     PISSN: 09500618     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conbuildmat.2010.01.006     Document Type: Article

References (18)

1. Mesbah H.A., Lacherni M., and Aitcin P.C. Determination of elastic properties of high performance concrete at early age. ACI Mater J 99 1 (2002) 37-41
2. Pauw A. Static modulus of elasticity of concrete as affected by density. ACI J 32 6 (1960) 679-687
3. ACI Committee 318. Building code requirements for reinforced concrete (ACI 318-95), American Institute, Farmington Hills, MI; 1995.
4. Turkish Standardization Institute. Requirements for design and construction of reinforced concrete structures TS 500. Ankara: Turkish Standardization Institute; 2000.
5. ACI Committee. 363 State-of-art-report on high strength concrete. ACI Mater J 81 4 (1984) 364-411
6. CEB-FIB Model Code. Bull. D'information CEB, No. 213/214, Lausanne; 1993.
7. Norwegian Council for Building Standardization. Concrete structures design rules NS 3473. Stockholm: Norwegian Concrete Association; 1992.
8. Fuat D. Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Constr Build Mater 22 (2008) 1428-1435
9. Fuat D. A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic. Cem Concr Res 35 (2005) 1531-1538
10. Vapnik V.N. An overview of statistical learning theory. IEEE Trans Neural Networks 10 5 (1999) 988-999
11. Vapnik V.N. Statistical learning theory (1998), Wiley, New York
12. Dong Y.F., Li Y.M., Lai M., and Xiao M.K. Nonlinear structural response prediction based on support vector machines. J Sound Vib 311 (2008) 886-897
13. Cortes C., and Vpanik V.N. Support-vector networks. Machines Learning 20 3 (1995) 233-297
14. Wee T.H., Chin M.S., and Mansur M.A. Stress-strain relationship of high-strength concrete in compression. J Mater Civ Eng 8 2 (1994) 70-76
15. Gesoglu M., Guneyisi E., and Ozturan T. Effects of end condition on compressive strength and static elastic modulus of very high strength concrete. Cem Concr Res 32 10 (2002) 1545-1550
16. Ozturan T. An investigation of concrete abrasion as two phase material. PhD thesis, Faculty of Civil Engineering, Istanbul Technical University; 1984 (in Turkish).
17. Turan M., and Iren M. Strain stress relationship of concrete. J Eng Arch 12 1 (1997) 76-81 (in Turkish)
18. Pijush S. Prediction of friction capacity of driven piles in clay using the support vector machine. Can Geotech J 45 (2008) 288-295