Computers and Concrete

Volume 17, Number 5, 2016, pages 629-648

DOI: 10.12989/cac.2016.17.5.629

Cost effective optimal mix proportioning of high strength self compacting concrete using response surface methodology

Asaduzzaman Khan, Jeongyun Do and Dookie Kim

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 439.4 kg/m3 cement content, 35.5% W/B ratio, 50.0% fine aggregate, 49.85 kg/m3 fly ash, and 7.76 kg/m3 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.

Key Words

central composite design; high strength self-compacting concrete; response surface method; optimization; desirability function

Address

Asaduzzaman Khan and Dookie Kim: Civil and Environmental Engineering, Kunsan National University,558 Daehak-ro, Gunsan-si 54150, Republic of Korea Jeongyun Do: Industry-University Cooperation Foundation, Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, Republic of Korea