Cost-optimized mix design of high-strength concrete: A model based on experimental regression and SQP optimization

Alireza Habibi , Mehdi Izadpanah , Amjad Jalali

Abstract

In light of increasing resource constraints and environmental concerns, optimizing the cost and performance of high-strength concrete (HSC) has become a key objective. This study introduces a cost-based optimization approach for HSC mix design using experimental data and nonlinear regression models. A dataset comprising 36 HSC mixes across three strength levels (50, 60, and 70 MPa) was used to develop predictive equations for compressive strength and slump. These models were integrated into a Sequential Quadratic Programming (SQP) framework to identify optimal mix proportions. Validation through laboratory tests confirmed the model's reliability, with optimal designs achieving a water-to-cement ratio of 0.3 and 10% silica fume content. The proposed method reduces material costs while meeting performance criteria and facilitating automated mix design processes.

Key Words

experimental data; optimum HSC mix design; sequential quadratic programming; slump prediction; strength prediction

Address

Alireza Habibi — Department of Civil Engineering, Shahed University, Tehran, Iran
Mehdi Izadpanah — Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran
Amjad Jalali — Department of Civil Engineering, University of Kurdistan, Sanandaj, Iran

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