Application of artificial neural networks for buckling prediction in functionally graded concrete sports structures and efficiency enhancement

This work describes a unique technique for forecasting the buckling behavior of functionally graded concrete (FGC) structures, with a focus on their use in sports engineering. Traditional prediction methods, which may rely on basic assumptions, fail to give the necessary accuracy for complicated material compositions. Artificial neural networks (ANNs) provide a versatile and adaptable approach to detecting complex patterns in FGC systems, particularly for sports infrastructure and equipment design. The ANN model displays versatility across different materials and structural designs, including stadium construction, sports equipment, and high-performance athletic surfaces, thanks to comprehensive training and validation on multiple FGC configurations. The ANN model exceeds standard analytical approaches in terms of speed and accuracy, demonstrating its effectiveness in anticipating crucial buckling stresses in dynamic, high-impact situations characteristic of sporting activities. This paper investigates the combination of artificial neural networks, image processing, and risk assessments, highlighting their importance in influencing design decisions. This work advances our understanding of the flexural properties of FGC structures, especially in athletic situations, allowing for the design of safer, more reliable, and performance-enhancing sports facilities. This technology offers engineers with an excellent tool for designing innovative and resilient sports-specific systems.