ML-Based Rupture Strength Assessment in Cementitious Materials

This paper presents an innovative machine learning-based approach for predicting concrete rupture strength, offering a faster and more cost-effective alternative to traditional testing methods. The proposed methodology employs a Random Forest Regressor (RFR) model, surpassing other regression models like Decision Tree Regressor (DTR) and Linear Regression (LR). A user-friendly web interface has been developed to facilitate practical implementation. In addition to highlighting the cutting-edge solution for predicting concrete rupture strength, the paper outlines avenues for future research, including dataset expansion, advanced model exploration, real-time monitoring through IoT, environmental considerations, and industry collaboration for deployment.