Prediction of the compressive strength of normal weight concrete with variable fly ash percentages using machine learning

This study explores how the strength of Ordinary Portland Cement (OPC) can be predicted, specifically looking at how different levels of fly ash impact it. The goal was to create models that can accurately predict OPC’s strength using machine learning techniques such as Linear Regression, Support Vector Machines (SVM), and Random Forest. Among these methods, Random Forest stood out as the most effective, significantly outperforming the other models. The dataset consisted of concrete samples with varying fly ash percentages and curing periods, with compressive strength as the target variable. Random Forest achieved the highest R² value of 0.7893 and the lowest Root Mean Squared Error (RMSE) of 6.5392, indicating its superior performance in predicting compressive strength compared to simpler models. The model’s ability to capture non-linear relationships made it well-suited to understanding the complex interactions between fly ash content, curing time, and strength. This research highlights the potential of using machine learning to optimize concrete mix designs for enhanced performance.