Reinforced concrete structural control subjected to design of fuzzy deep learning algorithm

This study explores the integration of fuzzy logic and deep learning algorithms for the structural control of reinforced concrete systems. As the demand for resilient infrastructure increases, traditional methods of structural analysis and control often fall short in addressing the complexities and uncertainties inherent in real-world applications. This research proposes a novel framework that combines fuzzy logic’s ability to handle imprecision with the powerful pattern recognition capabilities of deep learning. The fuzzy deep learning algorithm is designed to optimize the performance of reinforced concrete structures under various loading conditions, enhancing stability and safety. Through extensive simulations and experimental validations, the proposed method demonstrates significant improvements in predictive accuracy and robustness compared to conventional approaches. The findings highlight the potential of this hybrid methodology in advancing structural engineering practices, paving the way for smarter, more adaptive infrastructures in the face of dynamic environmental challenges. The objectives of this paper are access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable planning and management of human settlement. Therefore, the goal is believed to be achieved in the near future through the continuous development of AI and control theory for a better life from the environment and built systems.