Machine learning based grey wolf optimal controller of modified algorithm for reinforced concrete structures

Conventional concrete requires improvements in mechanical properties obtained with various mixtures. But making specific sample garments always takes time and money. In this article, we apply a different type of hybrid algorithm to develop a model that uses machine learning, fuzzy, neural network (NN), and Support Vector Machine (SVM). In this paper, the uncertain random excitation or disturbance are employed to input and identify systematic matrix including damping mass, stiffness, and other nonlinear forms. We developed a solid and systematic solution for the control and robustness design in machine learning based grey wolf optimal controller of modified algorithm for reinforced concrete structures unavailable in the existing literature. The goals of this paper are towards 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 human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.