JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Cost effective optimal mix proportioning of high strength self compacting concrete using response surface methodology
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Computers and Concrete
  • Volume 17, Issue 5,  2016, pp.629-638
  • Publisher : Techno-Press
  • DOI : 10.12989/cac.2016.17.5.629
 Title & Authors
Cost effective optimal mix proportioning of high strength self compacting concrete using response surface methodology
Khan, Asaduzzaman; Do, Jeongyun; Kim, Dookie;
 Abstract
Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be cement content, 35.5% W/B ratio, 50.0% fine aggregate, fly ash, and superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.
 Keywords
central composite design;high strength self-compacting concrete;response surface method;optimization;desirability function;
 Language
English
 Cited by
1.
Multi-objective optimization of TMD for frame structure based on response surface methodology and weighted desirability function, KSCE Journal of Civil Engineering, 2017  crossref(new windwow)
2.
Multiple tuned mass damper based vibration mitigation of offshore wind turbine considering soil–structure interaction, China Ocean Engineering, 2017, 31, 4, 476  crossref(new windwow)
3.
Experimental Optimization of High-Strength Self-Compacting Concrete Based on D-Optimal Design, Journal of Construction Engineering and Management, 2017, 143, 4, 04016108  crossref(new windwow)
 References
1.
Ahmad, S. and Alghamdi, S.A. (2014), "A statistical approach to optimizing concrete mixture design", The Scientific World J.

2.
Aitcin, P.C. (1998), High-Performance Concrete, E&FN SPON, New York, NY10001, USA.

3.
Alqadi, A.N., Mustapha, K.N., Al-Mattarneh, H. and Al-Kadi, Q.N. (2009), "Statistical models for hardened properties of self-compacting concrete", Am. J. Eng. App. Sci., 2(4), 764-770. crossref(new window)

4.
Alqadi, A.N., Mustapha, K.N., Naganathan, S. and Al-Kadi, Q.N. (2012), "Uses of central composite design and surface response to evaluate the influence of constituent materials on fresh and hardened properties of self-compacting concrete", KSCE J. Civil Eng., 16(3), 407-416 crossref(new window)

5.
Ahmadi-Nedushan, B. (2009), "Estimation of concrete compressive strength using advanced multivariate statistical techniques", Proceedings of the 8th International Congress on Civil Engineering, Shiraz, Iran, May.

6.
EFNARC (2002), Specification and Guidelines for Self Compacting Concrete, Association House, 99 West Street, UK.

7.
Li, M.C., Chen, Y.S., Chan, Y.W. and Hoang, V.L. (2012), "A study of statistical models application for mixture of high-flowing concrete", J. Mar. Sci. and Tec., 20(3), 325-335.

8.
Montgomery, D.C. (2012), Design and Analysis of Experiments, (8th Edition), Wiley, New York, USA.

9.
Murali, T.M. and Kandasamy, S. (2009), "Mix proportioning of high performance self-compacting concrete using response surface methodology", The Open Civil Eng. J., 3, 93-97. crossref(new window)

10.
Shanker, R. and Sachan, A.K. (2014), "Concrete mix design using neural network", Int. J. Civil Arch. Struct. Cons. Eng., 8(8), 883-886.

11.
Simon, M.J. (2003), Concrete Mixture Optimization Using Statistical Methods, Final Report FHWA-RD-03-060, Infrastructure Research and Development, Federal Highway Administration, Georgetown Pike McLean, Va, USA.