## 1 Introduction  
The maximum story drift of Multi-Degree of Freedom (MDOF) structures under wind loads is an important parameter for the structural design; however, for structural engineers, the estimation of this parameter by dynamic analysis could be impractical. The dynamic analysis of structures is complex and it requires real wind speed records, which are not available in most of the cases. Several studies suggest that the use of spectral density models are adequate to simulate the velocity field of the turbulent wind [1; 2; 3]. Due to the complexity of obtaining synthetic wind records and the dynamic structural response, the engineers have used non-complex methods such as the equivalent static analysis, which tries to consider the dynamic effects of the turbulent wind supported by the use of the well-known dynamic amplification factor [4]. Some modifications and improvements to the equivalent static method, originally presented by [PERSON] [5], have been proposed by different authors [6; 7]. Although these methods are not complex, they require several calculation steps in order to estimate variables such as power spectral density, background response, resonant response and others. The use of a prediction model that adequately represents a considerable amount of structures under wind loads could be useful. Nowadays, thanks to advances in computer hardware and software, Artificial Neural Networks (ANNs) have proven to be computational models capable of being applied in tasks as prediction, classification, data processing, robotic and engineering problems [8; 9; 10; 11]. For this reason, the aim of this study is to evaluate the efficiency of ANNs as computational models for prediction of the maximum story drift of MDOF structures in terms of regional wind speeds, terrain categories and structural characteristics such as height, slenderness ratio, fundamental period, distributed mass and stiffness ratio. The Newmark method is employed to resolve the differential equation of motion. This method is frequently used in numerical evaluation of the dynamic response of structures [12; 13; 14]. The wind loads applied in the dynamic analyses are generated from synthetic records obtained by spectral representation with the well-known Shinozuka method [15], which has been shown to give the best results in terms of overall quality of the signal [16]. The dynamic responses of a considerable amount of MDOF structures are used to build a database about the maximum story drift related to structural characteristics and wind conditions. This database is employed to the training processes of ANNs with the Levenberg-Marquardt (LM) method, which is a standard technique for solving nonlinear least squares problems [17]. The LM method is an optimization procedure with features of stable and fast convergence [18]. The training process is applied in ANN models known as multilayer perceptrons that consist of neurons located in multiple layers and interconnected to form a network with forward propagation (Figure 1a). The multilayer perceptron approach resolve problems that are not linearly separable, which is the main limitation of the standard linear perceptron method. [PERSON] [8] proved that one hidden layer of neurons suffices to model any solution surface of practical interest. In this paper, some multilayer perceptron models with different amounts of neurons in a single hidden layer are evaluated to predict the maximum story drift of 25,600 case studies. Figure 1b shows the internal mathematical process of each neuron. The output is given by an activation function f which depends on the sum of the inputs \(\mathrm{n}_{i}\mathrm{w}_{i}\):  
\[\mathrm{sum}=1\mathrm{w}_{0}+\mathrm{n}_{1}\mathrm{w}_{1}+\mathrm{n}_{2} \mathrm{w}_{2}+\ldots+\mathrm{n}_{i}\mathrm{w}_{i}, \tag{1}\]  
where \(\mathrm{n}_{i}\) is the output of other neuron and \(\mathrm{w}_{i}\) is the synaptic weight (Figure 1b).