Machine Learning Approach for the Flexural Strength of 3D‐Printed Fiber‐reinforced Concrete Based on the Meta‐heuristic Algorithm (2025-06)¶

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.