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The artificial neural network is such a model of biological neural networks containing some of their characteristics and being a member of intelligent dynamic systems. The purpose of applying ANN in civil engineering is their efficiency in some problems that do not have a specific solution or their solution would be very time-consuming. In this study, four different neural networks including FeedForward BackPropagation (FFBP), Radial Basis Function (RBF), Extended Radial Basis Function (ERBF), and Generalized Regression Neural Network (GRNN) have been efficiently trained to analyze large-scale space structures specifically double-layer barrel vaults focusing on their maximum element stresses. To investigate the efficiency of the neural networks, an example has been done and their corresponding results have been compared with their exact amounts obtained by the numerical solution.

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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.