Nonlinear transient deflections of multi-layer sector plate structures on auxetic concrete foundation: Introducing an artificial intelligence algorithm for nonlinear problems

This paper presents a comprehensive study on the nonlinear transient deflections of multi-layer sector plates, with a focus on presenting an artificial intelligence algorithm for addressing nonlinear problems in structural mechanics using the datasets of mathematical simulation. Multi-layer sector plates, commonly used in various engineering applications, exhibit complex nonlinear behaviors under external loading, particularly when coupled with unconventional materials such as auxetic concrete foundations. In this study, we propose the use of a mathematical simulation to analyze the nonlinear transient deflections of multi-layer sector plates on an auxetic concrete foundation. After that, a dataset (approximately 3750 data) is obtained and the algorithm is trained to capture the intricate nonlinear responses of the structure under different loading conditions. By leveraging an artificial intelligence algorithm, the algorithm can accurately predict the nonlinear behaviors of the multi-layer sector plate system, including vibration characteristics, dynamic response, and stability analysis. Through extensive numerical and validation studies, we demonstrate the effectiveness of the current mathematical modeling in accurately capturing the nonlinear transient deflections of multi-layer sector plates on auxetic concrete foundations. Furthermore, the proposed machine learning algorithm offers a promising approach for addressing nonlinear problems in structural mechanics, providing a versatile and efficient tool for engineers to analyze and optimize complex structural systems. By integrating machine learning techniques into structural analysis, researchers can enhance the accuracy and efficiency of nonlinear transient deflection studies, paving the way for advancements in structural engineering and related fields.