Next-Generation Hybrid Machine Learning Model for Dual Property Prediction in Sustainable Concrete

Abstract

Predicting concrete compressive strength (CS) and fire resistance (FR) is a crucial difficulty due to the high cost and time required for laboratory tests. This study introduces a hybrid machine learning system aimed at predicting these properties, utilizing nine models derived from 408 mix designs. The methodology utilized involved data cleaning, extensive feature engineering, k-fold cross-validation, and a separate test partition. An important advancement is the development of a dual-property prediction system that utilizes Explainable AI. The Stacking Ensemble and XGBoost algorithms exhibited remarkable accuracy, achieving R2 scores of 0.984 for CS and 0.985 for FR. The mean error recorded was 2.01 MPa for CS and 7.26 minutes for FR. The SHAP analysis clarified the results, identifying the water–cement ratio and cement content as the primary influential factors, consistent with principles of material science. A graphical user interface for a web application was developed to implement this predictive architecture for optimizing mixes in real time. This tool offers engineers prompt predictions, potentially decreasing design cycle times by 60–80% and minimizing dependence on physical testing, thereby promoting a data-driven approach in civil engineering.