Concrete is the most widely used constituent in the construction industry as a construction material due to its wide range of applications to civil infrastructure works. However, the use of concrete has been limited due to its certain deficiencies such as brittleness, low tensile strength, proneness to crack opening and propagation and low durability. To subdue these drawbacks, researchers have modified concrete by adding various synthetic and natural fibers to upgrade the nature of concrete. The demand for high strength and cracks resistant concrete led to the development of fiber-reinforced concrete. This paper reviews the effects of fibers inclusion on the performance of concrete. Generally, the addition of fibers improves tensile strength, flexural strength, and durability performance. Moreover, incorporating fibers reduces the shrinkage cracks of concrete. However, incorporating fibers in concrete has some negative effects like low workability.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.