Study on Self compacting Concrete using Ultrafine Fly ash

Abstract

Self-compacting concrete (SCC) is one of the concrete types that self-consolidates newlinewithout external influence. SCC is referred to as one of the highly performable concrete, as it facilitates concrete placement without sacrificing its fundamental properties. To facilitate this performance, the SCC possess higher cementitious content with the replacement of different mineral admixtures. Based on the previous pieces of literature, different industrial wastes, and natural and agro-waste materials are richer in lime and silica, and both have been used as a blend with cement at minimum to higher volumes. Based on their morphological and chemical compositions, the mineral admixtures are characterized as lime-rich, silica-rich, reactive/mechanochemically modified materials. Most of the studies have been carried out in SCC at both fresh and hardened states with these minerals. Based on the review utilization of UFFA in SCC and determining its exact ideal limits with consideration of its performance in SCC was very minor, which elucidated as research gap and novel findings. newlineBased on the experimental studies of SCC with UFFA demonstrated that the ideal limit of up to 20% blend with cement showed enhanced performance both in workability, stability, strength and resistance of the M40 grade SCC systems. 15% and 20% blends considered best SCC proportions, and SCC25 and SCC30 are considered excess dosages that lost their performance by loss of homogeneity and stability. Even at the microstructural level revealed similar results with the involvement of the UFFA densified the microstructure which attributes to improved compressive strength. Similar behavior was observed in the resistance properties and thermal analysis. newlineWhereas in the interconnected study of UFGFA in conventional grade concretes showed enhanced strength performance with 15% to 30% blended systems with lower w/b ratios less than 0.5. In the case of machine learning models, ANN models outperformed with R2 values of 0.9931 and 0.9971 for LMA and BA; RMSE