Prediction of mechanical properties of steel fibre-reinforced concrete under elevated temperature using artificial neural network techniques (ann) - Premkumar, G.; Selvan, S. Senthil (Frontiers in Built Environment, 2025)
This study investigates the mechanical performance of Steel Fibre-Reinforced Concrete (SFRC) subjected to elevated temperatures using artificial neural network (ANN) modeling. While existing literature mainly emphasizes the prediction of compressive strength, limited efforts have been made to predict other key mechanical properties under thermal stress. A comprehensive ANN framework was developed to simultaneously predict compressive strength, flexural strength, split tensile strength, and bond strength. The model was trained and validated using 967 experimental datasets encompassing a wide range of concrete mix designs and exposure conditions. The ANN architecture employed fully connected feedforward networks with ReLU activation in hidden layers and was individually optimized for each target parameter. The ANN model exhibited high predictive accuracy, with R² values of 0.85 (RMSE = 6.25 N/mm²) for compressive strength, 0.88 (RMSE = 5.74 N/mm²) for split tensile strength, 0.86 (RMSE = 6.18 N/mm²) for flexural strength, and 0.86 (RMSE = 6.08 N/mm²) for bond strength. These outcomes affirm the model's robustness in capturing complex nonlinear interactions between constituent materials, elevated temperature exposure, and mechanical behaviour. The proposed ANN-based framework provides an efficient and unified tool for predicting multiple mechanical properties of SFRC under thermal loading, requiring just 10 minutes for full analysis. This advancement fills a critical research gap and offers practical insights for the structural design of concrete in high-temperature environments.