Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach
Turki S. Alahmari, Jawad Ashraf, Md. Habibur Rahman Sobuz, Md. Alhaz Uddin
Abstract
Fiber-reinforced self-consolidating concrete (FR-SCC) combines the advantageous characteristics of self-compacting concrete with fiber reinforcement, providing a versatile solution for contemporary construction. However, due to its complexity and the scarcity of available data, the strength prediction techniques of FR-SCC are still in their early stages. To get around this limitation, research was done to create an optimal machine learning algorithm for predicting the compressive strength (CS) of FR-SCC. This work aims to precisely forecast the CS of FR-SCC by optimizing the parameters and structure of a Levenberg–Marquardt back propagation Artificial Neural Network (LMBP-ANN) model using K-fold cross-validation. One hundred twenty-three experimental data on FR-SCC from available literature was used to create the dataset. Several validation metrics, including coefficient of determination (R 2 ), mean absolute error (MAE), and root mean square error (RMSE) were employed to validate the models. Essential features that significantly impact the complex behavior of FR-SCC were found and incorporated into the model using multivariate analysis, Pearson correlation chart, and feature selection. The results show that K-fold cross-validation reduced training and testing errors by 22.2% and 18.3%. Consequently, an R 2 value of 0.9343 was achieved, which validated the model’s accuracy. SHAP analysis was also conducted in order to interpret the contribution of different features to the strength of FR-SCC. The most impactful feature was coarse aggregate, followed by curing age, superplasticizer, fly ash, and fiber content. The current work's findings might aid in precisely predicting the FR-SCC and the ANN network's design optimization procedure.