Sustainable self-consolidating green concrete: neural-network and fuzzy clustering techniques for cement replacement

ABSTRACT

This study investigates the properties of self-consolidating green concrete (SCGC) through experimental tests and employs artificial intelligence techniques for design parameter analysis. Cement is partially substituted with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Fresh and hardened properties tests are conducted. An adaptive neuro-fuzzy inference system (ANFIS) is developed to identify parameters influencing compressive strength. Seven ANFIS models evaluate input parameters individually, while twenty-one models assess different input combinations for optimization. Furnace slag significantly impacts hardened properties in binary mixes, while volcanic powder enhances slump retention. Ternary mix design with micro-silica and volcanic powder demonstrates substantial improvement. ANFIS results highlight binder content as the primary governing parameter for SCGC strength. The combination of micro-silica and volcanic powder exhibits superior strength compared to other additives, confirmed by test results. Overall, the study underscores the efficacy of incorporating micro-silica and volcanic powder for enhancing SCGC strength and sustainability.

Keywords:
Absorption; Durability; Fly ash; Gypsum; Quarry dust