Application of Carrera unified formulation and innovative artificial intelligence algorithm to study thermal buckling properties and structural optimization of fiber-reinforced concrete structures surrounded by auxetic foundations

This study investigates the thermal buckling behavior and structural optimization of multi-hybrid nanocomposite-reinforced concrete annular sector plates supported by auxetic foundations. Utilizing the Carrera unified formulation (CUF), a versatile higher-order theory is applied to capture the complex thermo-mechanical interactions in these advanced materials. The multi-hybrid nanocomposites are engineered by combining conventional reinforcement with nanoscale fillers to enhance stiffness, and thermal stability. The CUF framework facilitates the accurate representation of material behavior, geometric, and boundary conditions (BCs), allowing for a comprehensive analysis of the system's thermal buckling response. An innovative artificial intelligence (AI) algorithm is employed to validate and further refine the results obtained from the analytical models. This AI-based approach ensures robust verification and provides deeper insights into the parameter sensitivities affecting the structural performance. Additionally, the optimization process focuses on minimizing weight and maximizing thermal resistance while adhering to design constraints imposed by the auxetic foundation, known for its unique negative Poisson's ratio properties. The findings reveal the pivotal role of nanocomposite configurations and auxetic foundation properties in determining the critical buckling temperature and structural integrity. The integration of CUF with AI offers a powerful methodology for exploring complex design spaces, leading to enhanced performance and innovative solutions in engineering applications. This research bridges advanced material science, structural mechanics, and computational intelligence, paving the way for optimized designs in next-generation construction and aerospace systems.