A scientific investigation explores the application of machine learning to assess the compressive strength of red mud-based concrete, enhanced with fly ash, for potential use as a building construction material

Abstract

In construction engineering, concrete’s compressive strength is crucial but traditional production methods involve labor and finite resources. To address this, machine learning (ML) is gaining attention for predicting output parameters. This study focuses on using 5 ML models with 14 input parameters and a dataset of 500 points to predict compressive strength in red mud (RM)-based concrete. The Decision Tree (DT) and Extra Tree (ET) models performed best. Experimental results and microstructural analysis confirmed the adherence of RM-incorporated concrete to safety standards. Incorporating red mud offers potential for eco-friendly construction materials and sustainable waste management, especially for building construction.