PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS

Palika Chopra; Rajendra Kumar Sharma; Maneek Kumar

doi:10.24107/ijeas.251233

PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS

Year 2014, Volume: 6 Issue: 4, 10 - 22, 01.12.2014

Palika Chopra Rajendra Kumar Sharma Maneek Kumar

https://doi.org/10.24107/ijeas.251233

Cited By: 6

Abstract

A mathematical analysis, using statistical techniques, for prediction of compressive strength of concrete was performed for the concrete strength data obtained from experimental work conducted under standard conditions in the laboratory. The data on compressive strength was obtained separately for concrete mixes proportioned for medium and high workability. The variables used in the prediction models were the mix proportioning elements, which include water-cement ratio, aggregates to cement ratio, etc. The multiple non-linear regression models developed in this work yielded excellent CODs for prediction of compressive strength at different curing ages (28, 56 and 91 days). The regression model developed for experimental data was compared with those developed by other researchers as well. In general, it was found that both the models developed as a part of this study could predict the compressive strength at 28 and 91 days with more than 95% accuracy. Also, it can be concluded that for better prediction 91 days strength for both medium and high workability mixes, it is desirable to consider the 28 and 56 days strengths in the regression equations

Keywords

mathematical model, concrete, compressive strength, workability, multiple regression

References

[1] Akkurt, S., Tayfur G. and Can S., Fuzzy logic model for the prediction of cement compressive strength. Cement Concrete Research, 34, 1429-1433, 2004.
[2] Bayrak, H., and Akgül, F., Effect of coefficients of regression model on performance prediction curves.International journal of engineering and applied sciences, 5, 32-39,2013.
[3] Hwang, K., Noguchi, T. and Tomosawa, F., Prediction model of compressive strength development of fly-ash concrete. Cement Concrete Research, 34, 2269-2276, 2004.
[4] Kheder, G.F., Al-Gabban, A.M. and Suhad, M.A., Mathematical model for the prediction of cement compressive strength at the ages of 7 and 28 days within 24 hours. Materials and Structure. 36,693-701, 2003.
[5] Kumar, M., Reliability Based Design of Structural Elements’. Thesis submitted for degree of Doctor of Philosophy, T.I.E.T Patiala, Punjab, India 2003.
[6] Mehta, P.K. and Monteiro, P.J.M., Concrete, Microstructure, Properties and Materials. 3rd Edition. McGraw-Hill, USA, 2006.
[7] Namyong, J., Sangchun, Y. and Hong Bum, C., Prediction of compressive strength of concrete based on mixture proportions. Asian Architect Build. Engineering. 3, 9-16,2004.
[8] Popovics, S. and Ujhelyi, J., Contribution to the concrete strength versus water-cement ratio relationship. J. Mater. Civil Eng. 20,459-463,2008.
[9] Popovics, S., Analysis of concrete strength versus water-cement ratio relationship. ACI Mater. J., 87,517-529,1990.
[10] Popovics, S. and Popovics, J.S., Novel aspects in computerization of concrete proportioning, Concrete International. 54-58,1996.
[11] Sontag, E. D., Feedback stabilization using two-hidden-layer nets. IEEE Transactions on neural networks. 3, 981–990,1992.
[12] Steven, C., Raymond, C. and Canale, P., Numerical methods for engineers with personal computer applications. McGraw Hill, New York, 2002.
[13] Tsivilis, S. and Parissakis, G., A mathematical-model for the prediction of cement strength. Cement Concrete Research, 25, 9-14, 1995.
[14] Zain, M.F.M. and Abd, S.M., Multiple regressions model for compressive strength prediction of high performance concrete. Journal of Applied Sciences, 9, 155-160,2009.
[15] Zain, M.F.M., Mahmud, H.B., Ilham, A. and Faizal, M., Prediction of splitting tensile strength of high-performance concrete. Cement Concrete Research, 32, 1251-1258, 2002.
[16] Zelic, J., Rusic, D. and Krstulovic, R.,A mathematical model for prediction of compressive strength in cement-silica fume blends. Cement Concrete Research, 34, 2319-2328, 2004.
[17] ACI 214.3R-88, Simplified version of the recommended practice for evaluation of strength test results, ACI committee 214 report, ACI Materials Journal, Title no. 85-M33, pp. 272-279, 1988.
[18] IS: 383-1970 (Second Revision), Specifications for Coarse and Fine Aggregates from Natural Resources for Concrete.
[19] IS: 516 – 1959, Methods for Tests for Strength of Concrete.
[20] IS: 8112-1989, Specifications for High Strength Ordinary Portland Cement.

Year 2014, Volume: 6 Issue: 4, 10 - 22, 01.12.2014

Palika Chopra Rajendra Kumar Sharma Maneek Kumar

https://doi.org/10.24107/ijeas.251233

Cited By: 6

Abstract

References

[1] Akkurt, S., Tayfur G. and Can S., Fuzzy logic model for the prediction of cement compressive strength. Cement Concrete Research, 34, 1429-1433, 2004.
[2] Bayrak, H., and Akgül, F., Effect of coefficients of regression model on performance prediction curves.International journal of engineering and applied sciences, 5, 32-39,2013.
[3] Hwang, K., Noguchi, T. and Tomosawa, F., Prediction model of compressive strength development of fly-ash concrete. Cement Concrete Research, 34, 2269-2276, 2004.
[4] Kheder, G.F., Al-Gabban, A.M. and Suhad, M.A., Mathematical model for the prediction of cement compressive strength at the ages of 7 and 28 days within 24 hours. Materials and Structure. 36,693-701, 2003.
[5] Kumar, M., Reliability Based Design of Structural Elements’. Thesis submitted for degree of Doctor of Philosophy, T.I.E.T Patiala, Punjab, India 2003.
[6] Mehta, P.K. and Monteiro, P.J.M., Concrete, Microstructure, Properties and Materials. 3rd Edition. McGraw-Hill, USA, 2006.
[7] Namyong, J., Sangchun, Y. and Hong Bum, C., Prediction of compressive strength of concrete based on mixture proportions. Asian Architect Build. Engineering. 3, 9-16,2004.
[8] Popovics, S. and Ujhelyi, J., Contribution to the concrete strength versus water-cement ratio relationship. J. Mater. Civil Eng. 20,459-463,2008.
[9] Popovics, S., Analysis of concrete strength versus water-cement ratio relationship. ACI Mater. J., 87,517-529,1990.
[10] Popovics, S. and Popovics, J.S., Novel aspects in computerization of concrete proportioning, Concrete International. 54-58,1996.
[11] Sontag, E. D., Feedback stabilization using two-hidden-layer nets. IEEE Transactions on neural networks. 3, 981–990,1992.
[12] Steven, C., Raymond, C. and Canale, P., Numerical methods for engineers with personal computer applications. McGraw Hill, New York, 2002.
[13] Tsivilis, S. and Parissakis, G., A mathematical-model for the prediction of cement strength. Cement Concrete Research, 25, 9-14, 1995.
[14] Zain, M.F.M. and Abd, S.M., Multiple regressions model for compressive strength prediction of high performance concrete. Journal of Applied Sciences, 9, 155-160,2009.
[15] Zain, M.F.M., Mahmud, H.B., Ilham, A. and Faizal, M., Prediction of splitting tensile strength of high-performance concrete. Cement Concrete Research, 32, 1251-1258, 2002.
[16] Zelic, J., Rusic, D. and Krstulovic, R.,A mathematical model for prediction of compressive strength in cement-silica fume blends. Cement Concrete Research, 34, 2319-2328, 2004.
[17] ACI 214.3R-88, Simplified version of the recommended practice for evaluation of strength test results, ACI committee 214 report, ACI Materials Journal, Title no. 85-M33, pp. 272-279, 1988.
[18] IS: 383-1970 (Second Revision), Specifications for Coarse and Fine Aggregates from Natural Resources for Concrete.
[19] IS: 516 – 1959, Methods for Tests for Strength of Concrete.
[20] IS: 8112-1989, Specifications for High Strength Ordinary Portland Cement.

There are 20 citations in total.

Details

Other ID	JA66DA56ST
Journal Section	Articles
Authors	Palika Chopra This is me Rajendra Kumar Sharma This is me Maneek Kumar This is me
Publication Date	December 1, 2014
Published in Issue	Year 2014 Volume: 6 Issue: 4

Cite

APA	Chopra, P., Sharma, R. K., & Kumar, M. (2014). PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS. International Journal of Engineering and Applied Sciences, 6(4), 10-22. https://doi.org/10.24107/ijeas.251233
AMA	Chopra P, Sharma RK, Kumar M. PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS. IJEAS. December 2014;6(4):10-22. doi:10.24107/ijeas.251233
Chicago	Chopra, Palika, Rajendra Kumar Sharma, and Maneek Kumar. “PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS”. International Journal of Engineering and Applied Sciences 6, no. 4 (December 2014): 10-22. https://doi.org/10.24107/ijeas.251233.
EndNote	Chopra P, Sharma RK, Kumar M (December 1, 2014) PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS. International Journal of Engineering and Applied Sciences 6 4 10–22.
IEEE	P. Chopra, R. K. Sharma, and M. Kumar, “PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS”, IJEAS, vol. 6, no. 4, pp. 10–22, 2014, doi: 10.24107/ijeas.251233.
ISNAD	Chopra, Palika et al. “PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS”. International Journal of Engineering and Applied Sciences 6/4 (December 2014), 10-22. https://doi.org/10.24107/ijeas.251233.
JAMA	Chopra P, Sharma RK, Kumar M. PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS. IJEAS. 2014;6:10–22.
MLA	Chopra, Palika et al. “PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS”. International Journal of Engineering and Applied Sciences, vol. 6, no. 4, 2014, pp. 10-22, doi:10.24107/ijeas.251233.
Vancouver	Chopra P, Sharma RK, Kumar M. PREDICTING COMPRESSIVE STRENGTH OF CONCRETE FOR VARYING WORKABILITY USING REGRESSION MODELS. IJEAS. 2014;6(4):10-22.