Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN

In the present research, the information on compressive strength of self-compacting concrete (SCC) containing rice husk ash (RHA) and calcium carbide waste (CCW) as an admixture cured for 28 days was provided. The research applied feedforward propagation neural network (FFNN), emotional neural network (EANN), and conventional linear regression (LR) in the prediction of compressive in which FFNN, EANN, and LR models were trained on the experimental data obtained from addition of 0%–10% RHA and 0%–20% CCW in the SCC mixtures. The results revealed that inclusion of CCW reduces the workability of SCC mixtures and increases in compressive strength at 28 days were observed for SCC mixture containing 10% RHA and 0% CCW against the reference mixtures. The results also indicated that all the AI models (FFNN, EANN, and LR) performed very well with R²-values higher than 0.8951 in both the testing and training stages. The results showed that EANN-M3, FFNN-M3, and LR-M3 combination has the highest performance evaluation criteria of R² = 0.9733 and 0.9610, R² = 0.9440 and 0.9454 and R² = 0.9117 and 0.9205 in both training and testing stages, respectively. It indicates the proposed models' high accuracy in predicting the compressive strength σ of self-compacting concrete with rice husk ash as cement replacement and calcium carbide waste as supplementary materials. The result also suggested that other models, like emerging algorithms, hybrid models, and optimization methods, could enhance the models’ performance.