Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks
Publication: Journal of Materials in Civil Engineering
Volume 26, Issue 11
Abstract
High-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0–15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.
Get full access to this article
View all available purchase options and get full access to this article.
References
Akkurt, S., Tayfur, G., and Can, S. (2004). “Fuzzy logic model for the prediction of cement compressive strength.” Cem. Concr. Res., 34(8), 1429–1433.
American Concrete Institute (ACI) Committee 211. (1993). “Guide for selecting proportions for high strength concrete with portland cement and fly ash.” ACI211.4R-93, Detroit, MI.
American Concrete Institute (ACI). (1997). State-of-the-art report on high-strength concrete, Committee 363, Detroit.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology I: Preliminary concepts.” J. Hydrol. Eng., 115–123.
ASTM. (2002a). “ASTM C494/C494M-12 standard specification for chemical admixtures for concrete.” Annual book of ASTM standards, West Conshohocken, PA.
ASTM. (2002b). “Standard specification for portland cement.” Annual book of ASTM standards, West Conshohocken, PA.
Dias, W. P. S., and Pooliyadda, S. P. (2001). “Neural networks for predicting properties of concretes with admixtures.” Constr. Build. Mater., 15(7), 371–379.
Erdem, T. K., and Kirca, Ö. (2008). “Use of binary and ternary blends in high strength concrete.” Constr. Build. Mater, 22(7), 1477–1483.
Erdem, T. K., Tayfur, G., and Kirca, Ö (2011). “Experimental and modeling study of strength of high strength concrete containing binary and ternary binders.” Cem. Wapno Beton, 16(4), 224–237.
Fa-Liang, G. (1997). “A new way of predicting cement strength—Fuzzy logic.” Cem. Concr. Res., 27(6), 883–888.
Hong-Guang, N., and Ji-Zong, W. (2000). “Prediction of compressive strength of concrete by neural networks.” Cem. Concr. Res., 30(8), 1245–1250.
Jantzen, J. (1999). “Design of fuzzy controllers.”, Dept. of Automation, Technical Univ. of Denmark, Lyngby, Denmark.
Kasperkiewicz, J., Racz, J., and Dubrawski, A. (1995). “HPC strength prediction using artificial neural network.” J. Comput. Civ. Eng., 279–284.
Mamdani, E. H. (1977). “Application of the fuzzy logic to approximate reasoning using linguistic synthesis.” IEEE Trans. Comput., C-26(12), 1182–1191.
MATLAB [Computer software]. Natick, MA, MathWorks.
Mehta, P. K., and Monteiro, P. J. M. (2006). Concrete microstructure, properties and materials, 3rd Ed., McGraw-Hill, New York.
Özcan, F., Atis, C. D., Karahan, O., Uncuoğlu, E., and Tanyıldızı, H. (2009). “Comparison of artificial neural network and fuzzy logic models for prediction of L-term compressive strength of silica fume concrete.” Adv. Eng. Software, 40(9), 856–863.
Öztaş, A., Pala, M., Özbay, E., Kanca, E., Çağlar, N., and Bhatti, M. A. (2006). “Predicting the compressive strength and slump of high strength concrete using neural network.” Constr. Build. Mater., 20(9), 769–775.
Sarıdemir, M. (2009). “Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic.” Adv. Eng. Software, 40(9), 920–927.
Şen, Z. (1998). “Fuzzy algorithm for estimation of solar irradiation from sunshine duration.” Sol. Energy, 63(1), 39–49.
Şen, Z. (2004). Fuzzy logic and system models in water sciences, Turkish Water Foundation, İstanbul, Turkey.
Sugeno, M. (1985). Industrial applications of fuzzy control, Elsevier Science, Amsterdam, The Netherlands.
Tayfur, G. (2006). “Fuzzy, ANN, and regression models to predict longitudinal dispersion coefficient in natural streams.” Nordic Hydrology, 37(2), 143–164.
Tayfur, G. (2012). Soft computing in water resources engineering, WIT, Southampton, England, U.K.
Topçu, İ. B., and Sarıdemir, M. (2008). “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic.” Comput. Mater. Sci., 41(3), 305–311.
Yeh, I.-C. (1998). “Modeling of strength of high-performance concrete using artificial neural networks.” Cem. Concr. Res., 28(12), 1797–1808.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 26 • Issue 11 • November 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Apr 9, 2012
Accepted: Nov 15, 2013
Published online: Nov 18, 2013
Published in print: Nov 1, 2014
Discussion open until: Nov 2, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.