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This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the outputs are the MR damper voltages in the current step. The required training data for the neural controller is generated using genetic algorithm (GA). Using the El-Centro earthquake data, GA calculates the optimal damper force at each time step. The optimal voltage is obtained using the inverse model of the Bouc-Wen based on the predicted force and the corresponding velocity of the MR damper. This data is stored and used to train a multi-layer perceptron neural network. The ANN is then employed as a controller in the structure. To evaluate the efficiency of the proposed method, three- story, seven- story and twenty-story structures with a different number of MR dampers were subjected to the Kobe, Northridge, and Hachinohe earthquakes. The maximum reduction in structural drifts in the three-story structure are 13.05%, 39.90%, 15.89%, and 8.21%, for the El-Centro, Hachinohe, Kobe, and Northridge earthquakes, respectively. As the control structure is using a pre-trained neural network, the computation load in the event of an earthquake is extremely low. Additionally, as the ANN is trained on seismic pre-step data to predict the damper's current voltage, the influence of time lag is also minimized.

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In this study, the response of semi-actively controlled structures is investigated, with a focus on the effects of magneto-rheological (MR) damper distribution on the seismic response of structures such as drift and acceleration. The proposed model is closed loop, and the structure's response is used to determine the optimal MR damper voltage. A Fuzzy logic controller (FLC) is employed to calculate the optimum voltage of MR dampers. Drifts and velocities of the structure’s stories are used as FLC inputs. The FLC parameters and the distribution of MR dampers across stories are determined using the NSGA-II, when the structure is subjected to the El-Centro earthquake, so as to minimize the peak inter-story drift ratio and peak acceleration simultaneously. The efficiency of the proposed approach is illustrated through a twenty-story nonlinear benchmark structure. Non-dominated solutions are obtained to minimize the inter-story drift and acceleration of structures and Pareto front produced. Then, the non-dominated solutions are used to control the seismic response of the benchmark structure, which was subjected to the Northridge, Kobe, and Hachinohe earthquake records. In the numerical example the maximum drift and acceleration decrease by about 36.3% and 15%, respectively, in the El-Centro earthquake. The results also demonstrate that the proposed controller is more efficient in reducing drift than reducing acceleration.