## 1 Introduction  
The conventional method to simulate the time history response of a building model subjected to dynamic loadings, such as earthquake ground motion and wind load, is based on the principle of mechanics. [PERSON]'s laws of motion are the essence behind the establishment of the equilibrium equations. When considering the complexity of the original formulation and the development of numerical computation resources in the second half of the 20 th century, the resulting second-order differential equations had been reformulated and numerically solved through discretization in space and time using the Finite Element Method (FEM) and time integration algorithms, respectively. Many analytical models to simulate the nonlinear behavior of common structural members, such as reinforced concrete (RC) structural components, had already been formulated and validated by experimental results [1]. The combination of all these modules makes it possible to compute a building response by performing the nonlinear time history analysis (NTHA). Many assumptions and/or simplifications are considered, whether in the original physics laws, the FEM, or the hysteresis models of structural components. Time integration methods also generate numerical errors, and their stability may be compromised [2, 3]. Despite all these inevitable limitations, the NTHA remains the most accurate and reliable mechanics-based method to evaluate the seismic performance of a structural model. This know-how must be the reference for response prediction problems (RPPs) of building structures. In the last decade, data-driven methods took an impetus in several applications in earthquake engineering. The latter can be categorized in three sub-fields: RPP [4], damage evaluation [5] (DE), and system identification [6] (SI). [PERSON] et al. [7] conducted an exhaustive state-of-the-art review of the implementation of machine learning (ML) in both earthquake engineering and seismology. For RPPs, machine learning models (MLMs) are generated using regression ML algorithms such as support vector regression, random forest, response surface model, and artificial neural networks (ANNs). The development of these surrogate models needs data. The input data may be physical building information (number of stories, structural materials, lateral force resisting system...), building modal properties (natural periods, modal shapes...), ground motion data (time-history, frequency content...), and/or other variables. The output data are the response of interest (acceleration, shear, drift...) to predict for specific degrees of freedom of the studied structure, depending on the problem framing. Hybrid MLMs have been proposed by stacking different ML algorithms [8], by combining different types of inputs such as structural analysis response quantities and simply measured or evaluated building properties [9], and/or by incorporating some physics-based principles into the design of the ML algorithm [10]. During the training phase, an ML algorithm forms a mapping between input and output data that could predict the response of interest for new unseen data, all of the same nature as the original training data. Hence, these black-box models can predict only limited response quantities of specific structure(s), and their accuracy and reliability are highly dependent on the nature and amount of data used during their training. Therefore, some skepticism regarding their application is still present. For RPPs, deep learning (DL) models such as multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are becoming more attractive since no need to identify features within input data are required, and more sophisticated DL algorithms are being constantly developed [11]. Even though RNNs were initially designed for time series forecasting problems, only very few of their applications are counted in earthquake engineering. [PERSON] et al.