Optimizing self-compacting concrete: A comprehensive analysis of weighting methods and multi-response optimization

Abstract

• Integrates Taguchi-TOPSIS for optimal Self-Compacting Concrete formulation. • Compares five objective weighting methods for concrete rheology optimization. • CRITIC method most effectively captures criterion variation and correlation. • Provides reliable experimental data on sustainable concrete admixtures. • Proposes optimized mix designs meeting EFNARC criteria for SCC performance. This research examines the optimization of Self-Compacting Concrete (SCC) mixes with objective Multi-Criteria Decision Making (MCDM) methods assisted by experimental rheological testing. 28 SCC mixes with different superplasticizer (SP), viscosity-modifying agent (VMA), limestone filler (LF), and silica fume (SF) dosages were manufactured. The fresh properties were analyzed using slump flow, V-funnel, l -box, sieve segregation tests, and plastic viscosity and yield stress measurements with the rheometer. Five objective weighting methods (Entropy, Mean, Standard Deviation, Statistical Variance, and CRITIC—were used for ranking rheological parameters, then optimization was realized using the Taguchi-TOPSIS technique. Results validated that the mixes with higher dosages of SP (0.9–1.0 %) and elevated content of LF (35 %) possessed significantly improved rheological properties by attaining >85 cm of slump flows, reduced V-funnel times ranging between 8 and 11 s, 1.00 L-box ratio, and smaller yield stress and viscosity compared to other blends. Specifically, SP1.0 was the best-performing blend since it posted 75 % decrease in yield stress and 65 % improvement in flowability over base mix reference values. CRITIC weight assignment was likewise most sensitive to data uncertainty to provide consistent optimizing solutions. Furthermore, sensitivity tests confirmed the rank order stability of highest-ranked blends for ±10 % variation in the weights of the criteria. Such findings underscore the imperative of integrating experimental testing with decision-making approaches grounded in objectivity to achieve viable and high-performing SCC designs.