Abstract
The increasing demand for concrete in construction presents challenges such as pollution, high energy consumption, and complex structural requirements. Three-dimensional printing (3DP) offers a promising solution by eliminating formwork, reducing waste, and enabling intricate geometries. Predicting the strength of 3D-printed fiber-reinforced concrete (3DP-FRC) remains challenging due to the nonlinear nature of neural networks and uncertainty in optimizing key parameters. In this study, we developed machine learning models using five metaheuristic algorithms-arithmetic optimization algorithm, African Vulture Optimization Algorithm, flow direction algorithm, generalized normal distribution optimization, and Mountain Gazelle Optimizer-to optimize the weights and biases in a feed-forward backpropagation network. Among all the algorithms, MGO demonstrated the best performance. To address data limitations, a data augmentation method combining Kernel density estimation and Wasserstein generative adversarial networks is employed. Sensitivity analysis using SHapley Additive exPlanations (SHAP) identifies the most influential input parameters. The proposed MGO-ANN model enhances predictive accuracy, reducing the need for extensive laboratory testing. Additionally, a user-friendly graphical user interface is developed to facilitate practical applications in estimating 3DP-FRC flexural strength.