ANN-based prediction and experimental evaluation of sustainable fibre-reinforced concrete incorporating sugarcane bagasse ash and limestone slurry ash

The study investigated the potential of producing sustainable concrete by partially replacing cement with varying proportions of sugarcane bagasse ash (SCBA) at 0%, 10%, 20%, and 30%, along with limestone slurry ash (LSA) at 0%, 2.5%, 5%, 7.5%, and 10%. Experimental results indicated that the optimum performance was achieved with a mix containing 20% SCBA and 5% LSA. Based on these findings, an additional investigation was carried out using the optimum combination along with 2% polypropylene fibre reinforcement to further improve the mechanical properties of the concrete. Compared to conventional concrete, the developed mixes containing SCBA, LSA, and polypropylene fibres exhibited considerable enhancement in mechanical performance. The optimum mix achieved a compressive strength of 29.2 MPa, exceeding the control concrete strength of 27.45 MPa. Similarly, notable improvements were observed in split tensile and flexural strengths, which reached 3.21 MPa and 6.21 MPa, respectively. The experimental results suggest that the combined use of SCBA and LSA provides effective cementitious properties, leading to improved strength characteristics and durability. Furthermore, the incorporation of polypropylene fibres enhanced crack resistance and improved the stability of the concrete matrix, resulting in higher tensile and flexural performance. In addition to the experimental investigation, an Artificial Neural Network (ANN) model was developed using the optimum mix parameters for predictive analysis.