Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

The construction of durable and sustainable infrastructure requires the use of industrial byproducts such as fly ash (FA) and silica fume (SF) to enhance strength and durability. This study introduces novel machine learning models to forecast the results of rapid chloride penetration test (RCPT) for self-compacting concrete (SCC) containing high volumes of FA and SF. This study assessed the effects of supplementary cementitious materials (SCMs) and elevated temperature curing on the RCPT outcomes for SCC. Various metaheuristic algorithms including teaching–learning-based optimization (TLBO), ant colony optimization (ACO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE)—optimize the learning rate, weights, and biases of artificial neural network (ANN) models. A dataset of 360 experimental RCPT data points with seven input parameters was used to train and test the hybrid models. The accuracy of these models was assessed using eight performance indices, and the results were further analyzed through rank analysis, scatter plots, and error matrices. The training and testing sets for the AI models, specifically ANN-TLBO, exhibit a strong correlation between the experimental and predicted RCPT values, with R2 values of 0.9962 for training and 0.9676 for testing, compared to the R2 values of the other proposed models. Consequently, the results of this study suggest that the ANN-TLBO model is the optimal hybrid ANN model for predicting RCPT values in SCC. Verified experimental results and external validation indicate that the ANN-TLBO model is an effective alternative for accurately predicting real-time RCPT in SCC.