## 1 Introduction  
Much research on the development of seismic response reduction technologies has been conducted to date. Various types of active, semi-active, and passive control systems have provided good seismic response reduction. Although active control devices can best reduce seismic responses, practicing engineers have yet to fully embrace them, in large part because of the challenges of large power requirements, and concerns about their stability and robustness. Because of these defects of active control systems, research on semi-active control systems is being actively conducted [1; 2; 3]. Semi-active control devices have been applied to various types of control systems, such as the tuned mass damper [4; 5], base or mid-story isolation system [6; 7], outrigger damper system [8], coupled building control system [9], and bracing system with dampers [10]. Once the application plan of the semi-active control system for building structures subjected to earthquake loads is decided, a control algorithm is one of the most important factors to affect seismic response reduction performance. Therefore, a number of studies on semi-active control strategies have been carried out by researchers [11; 12; 13]. Many numerical simulations should be carried out to evaluate the control performance of the semi-active control algorithm. In particular, the soft computing-based control algorithms, such as fuzzy logic controller (FLC), genetic algorithm (GA), and artificial neural network (ANN), require many more time history analysis runs to find the global optimal solution [14; 15]. Usually, a state-spacemodel of the building structure is used to obtain the structure's dynamic responses under various excitations, considering the semi-active control forces [16]. Thanks to this representative state-space model, simulation tests of semi-active control efficiency and robustness can be realized. The state-space model is built according to the finite element (FE) model, namely the mass, damping, and stiffness matrices, exclusively. The differential equations should be solved to obtain the dynamic responses of the building structure using a state-space model. If the number of degrees of freedom (DOF) of the EF model is large, or the time-step of the state-space analysis is small for stable solving of the differential equations, the computational time for simulation tests of the semi-active control system could be considerable. In this case, the solution search area of soft computing techniques including evolutionary algorithm may be narrowed to significantly reduce the optimization time, resulting in failure to find the global optimal solution. To solve this problem, the effective dynamic response simulation model of the building structure with a semi-active control system is developed using deep learning technique. At present, almost every industry is affected by artificial intelligence (AI). Since an artificial neural network (ANN) has been applied to structural engineering, many studies on various topics have been conducted [17]. Recent rapid advances in deep learning have greatly expanded its application possibility to structural engineering, such as design optimization [18], structural system identification [19], structural assessment and monitoring [20], structural control [21], and finite element generation [22]. Among these topics, this study focused on structural system identification and structural control. Previous studies have successfully used ANN, fuzzy inference system, adaptive neuro fuzzy inference system, etc. to present the dynamic characteristics of the structural control device [23; 24]. However, research on the dynamic response prediction of an entire building structure with semi-active control devices has rarely been performed to date. Several types of neural networks (NNs) are used in various engineering research areas. Feed-forward neural network (FNN) is one of the simplest forms of ANNs. The application of FNNs is found in computer vision and speech recognition.