A RSM-Based Multi-Response Optimization Application for Determining Optimal Mix Proportions of Standard Ready-Mixed Concrete

ŞİMŞEK B., İÇ Y. T., ŞİMŞEK E. H.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, cilt.41, sa.4, ss.1435-1450, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1007/s13369-015-1987-0
  • Dergi Adı: ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1435-1450
  • Anahtar Kelimeler: Central composite design, Convection heat transfer coefficient, Factor effects, Standard ready-mixed concrete, Response surface methodology, HIGH-STRENGTH CONCRETE, MECHANICAL-PROPERTIES, NEURAL-NETWORK, TAGUCHI METHOD, HEAT-TRANSFER, SILICA FUME, METHODOLOGY, DESIGN, DEGRADATION, PERFORMANCE
  • Ankara Üniversitesi Adresli: Evet

Özet

In this study, design of experiment methodology was applied to the optimization of mixing the proportions of standard ready-mixed concrete (SC). The mixture proportion modeled by using response surface methodology (RSM) was determined as the function of variables such as aggregate mixture ratio, water-to-cement ratio and the percentage of super plasticizer content. The results show that water-to-cementitious material ratio causing the highest variation in responses is the most important factor, and aggregate mixture ratio is designated as the second most important factor. The results show also that the responses of convection heat transfer coefficient and the percent of air content are significantly affected by the synergistic effect of linear term of water-to-cement ratio and the antagonistic effect of quadratic term of water-to-cement ratio. Early comprehensive strength is significantly affected by the synergistic effect of linear term of ratio of water to cement materials and the synergistic effect of linear terms of aggregate mixture rate. Finally, three variables formulated using regression analyses were simultaneously optimized by utilizing RSM-based desirability function method.