Collaborative enhancement of concrete performance: integrating sugarcane bagasse ash and iron ore tailing using artificial neural networks

Abstract

Abstract The aim of this study is to utilize Artificial Neural Networks to predict the compressive strength of concrete. The cement component is replaced with Sugarcane bagasse ash, the sand component is replaced with Iron Ore tailings, and a combination of both Sugarcane bagasse ash and Iron Ore tailings is used as substitutes for cement and sand. The strength of the concrete is evaluated after curing periods of 7, 14, and 28 days through experimental analysis. The Artificial Neural Networks model is trained using four input parameters and one output parameter based on the collected data. The study incorporates various percentages of Sugarcane bagasse ash (ranging from 0% to 25%) and Iron Ore tailings (ranging from 0% to 50%), as well as combinations of Sugarcane bagasse ash of various percentages with 10% Iron Ore tailings. The predictions generated by the Artificial Neural Networks model demonstrate a strong correlation with the experimentally obtained data. These findings highlight the effectiveness of Artificial Neural Networks as a predictive model for determining the strength of concrete that incorporates industrial by-products as substitutes for cement and sand.