A Novel Evolutionary Algorithm-Based Approach for Optimizing the Mix Design of High-Performance Fiber-Reinforced Concrete (HPFRCC)

With the increasing demand for high-performance concrete, optimizing the design of high-performance alloy-armed compounds (HPFRCC) has become a major challenge. Optimizing the mix design enables the achievement of the desired stability and ductility. This study offers an innovative way to overcome this challenge using artificial intelligence (AI). Our method uses artificial neural networks (ANN) to model the relationship between the mixed component and final HPFRCC properties. Then, we explored a wide design space using advanced distribution algorithms to determine the best combination. These algorithms are inspired by nature, such as the evolutionary algorithm (EO), which replicates the scientific evolutionary process. This study significantly reduces the need for laboratory testing by employing innovative methodologies. This significant achievement significantly optimizes design time and cost. In addition, by comprehensively comparing EO performance with other similar algorithms, the excellence and innovation of the method were demonstrated in optimizing the complex design of the HPFRCC mixture, and a new multitarget optimization algorithm called FC-MOEO/AE was used to predict the HPFRCC combination. This method can be used as a design guide to guide decision-making in the pre-construction stage.