The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller’s superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure’s characteristics change.

This paper presents a novel adaptive neurochaotic fuzzy control system based on type‐2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near‐field and far‐field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back‐propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional‐order proportional‐integral‐derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.

The primary objective of this study is to introduce a novel adaptive fractional order proportional–integral–derivative (FOPID) controller. The adaptive FOPID controller’s parameters are dynamically adjusted in real-time using five distinct multilayer perceptron neural networks. The extended Kalman filter (EKF) is employed to facilitate the parameter-tuning process. A multilayer perceptron neural network, trained using the error Backpropagation algorithm, is employed to identify the structural system and estimate the plant. The real-time estimated Jacobian is applied to the controller to control the model. The stability and robustness of the adaptive interval type-2 fuzzy neural networks controller are enhanced by utilizing the EKF and the feedback error learning strategy for compensator tuning. This improvement increases resilience against estimation errors, seismic disturbances, and unknown nonlinear functions. The primary objective is to address the challenges posed by maximum displacement, acceleration, and drift, as well as the uncertainties arising from variations in stiffness and mass. In order to validate the reliability of the proposed controller, the performance investigation is carried out on an 11-story building equipped with an active tuned mass damper under far and near-field earthquakes. Numerical findings show the remarkable effectiveness of the proposed controllers compared to their predecessors. In addition, it is revealed that the inclusion of the adaptive interval type-2 fuzzy neural networks compensator has increased the performance of the proposed controller and shows significant capabilities in reducing the seismic responses of structures during severe earthquake events.

The construction industry continually seeks to optimize the materials used in building projects to balance structural integrity with economic and environmental concerns. Particularly in urban high-rise construction and seismic zones, the choice of material can significantly affect the project’s feasibility and safety. This study investigated the mechanical response of regular and lightweight concrete to bending stresses, which are critical factors in the structural design process. By comparing the strain–stress relationships, densities, and deformation capacities of these two varieties of concrete, this research aimed to clarify their fundamental differences. Lightweight concrete, with its lower density of 1800 kg/m³, offers noticeable weight reductions, thus presenting a viable solution for structures where reduced mass is beneficial, such as in areas with load-bearing limitations or for components that require ease of transportation and assembly. However, this study found that the reduction in weight is accompanied by lower compressive and flexural strength compared to normal concrete, which, with a density of 2400 kg/m³, exhibited greater mechanical strength and resilience under bending stresses. Stress–strain graphs generated from the experimental data illustrated both types of concrete’s elastic and plastic deformation behaviors, highlighting the material’s transition from elasticity to plasticity at specific stress points. This nuanced understanding of the materials’ behaviors is vital for construction professionals as it equips them with the information necessary to make informed decisions on material selection, ensuring safety, sustainability, and cost-effectiveness in construction projects.