Abstract
This research introduces a novel approach for optimizing structural resilience in smart building construction, focusing on enhancing both dynamic performance and energy efficiency. The study investigates a sandwich plate structure, where the core is made of functionally graded (FG)-triply periodic minimal surfaces (TPMS) material, providing superior mechanical properties. The face sheets are equipped with sensors and actuators, forming an active system capable of real-time monitoring and adaptive response to external forces. To further improve the system’s stability, a viscoelastic-auxetic foundation is incorporated, offering enhanced damping characteristics and the ability to absorb vibrations more efficiently. The ultimate goal is to optimize the natural frequency of the sandwich plate to prevent resonance under dynamic loading conditions, ensuring long-term durability, and performance. The structural model is developed using the Carrera unified formulation (CUF), a high-order finite element method that accurately represents complex geometries and deformation behavior, enabling precise simulations of the dynamic response. Artificial intelligence (AI) techniques are then employed to validate the model, optimizing parameters through machine learning algorithms to enhance the efficiency of the structural system. This new methodology combines advanced materials with cutting-edge computational tools to offer a significant improvement in the construction of smart buildings. By optimizing both structural resilience and energy efficiency, this approach provides a pathway to more sustainable and durable building designs, marking a step forward in the future of intelligent, high-performance construction technologies.
Published Version
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