This paper proposes a wavelet-based fuzzy neuro control algorithm for the hazard mitigation of seismically excited buildings equipped with a hybrid control system. The wavelet-based fuzzy neuro controller is developed through the integration of discrete wavelet transform, artificial neural network, and a Takagi-Sugeno fuzzy controller. The hybrid control system is an integrated model of an actuator, a tuned mass damper, and viscous liquid dampers: an active tuned mass damper (ATMD) is located on the top floor of the structure and viscous liquid dampers are located on each floor. To demonstrate the effectiveness of the proposed wavelet-based adaptive neuro-fuzzy inference system (WANFIS) controller, an eight-story building employing passive viscous liquid dampers as well as an ATMD is investigated. A variety of earthquakes such as an artificial earthquake, the 1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes are used as disturbance signals. It is demonstrated that the WANFIS controller is effective in reducing the structural responses of the hybrid structure system subjected to a variety of disturbances.

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.