# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
### AI in Earthquake Engineering  
One of the most significant areas of scientific research has always been how to employ machine learning models to extract trustworthy, worthwhile, and accurate information from vast volumes of data. As a nonparametric modeling method, artificial neural networks (ANNs) are a powerful tool for solving a wide range of challenging nonlinear engineering problems, because they can approximate any linear and nonlinear functional relations with arbitrary precision [22; 23]. Previous studies have shown that ANNs can be used to effectively extract potential mapping relationships from large datasets. They are widely used in the fields of structural engineering, ocean engineering, and bridge engineering [24; 25; 26]. In earthquake engineering, ANNs are used to efficiently identify the hidden causality from massive structured data for application in earthquake prediction, ground motion modeling, and structural response analysis [27; 28; 29; 30]. [PERSON] et al. [31] proposed a BAT-ANN model to predict earthquakes. On the basis of past earthquake data, the weight of the model can be updated by the bat algorithm (BA) so that the feed-forward neural network (FFNN) can predict future earthquakes. [PERSON] et al. [32] used an ANN and a large number of ground motion records to build a prediction model of ground motion intensity for active shallow crustal earthquakes in India. The input variables of the model were the magnitude (M), hypocentral distance (R), site condition (S), and flag for the region (f), and the output variables were 21 ground motion parameters (GMPs) in both the horizontal and vertical directions. [PERSON] et al. [33] used label datasets from 47 cities in Indonesia (different building heights, soil conditions, and seismic locations) and a backpropagation neural network (BPNN) to predict the structural responses, such as displacement, velocity, and acceleration, of multi-story buildings with a fixed floor plan. The RNN is a special kind of ANN for time-series or sequential data [34; 35]. The model generates the prediction sequence from the input information, which is very suitable for the response prediction of nonlinear structures under random seismic loads. However, when dealing with lengthy or highly nonlinear sequence data, the RNN can suffer from problems, such as gradient vanishing or gradient explosion, which cause the model to cease learning. In order to overcome these problems, the concept of \"gate\" was added on the basis of the RNN, and the LSTM was proposed [36]. RNNs and LSTMs are widely used in predicting the fatigue life of materials, damage monitoring, stock price index forecasting in finance, credit card fraud risk forecasting, and so on. They also have good applications in the field of earthquake engineering. [PERSON] et al. [37] used several indicators to assess the susceptibility of seismic wave amplification by predefined weights of layers, and then applied 10 indicators to a developed recursive neural network model to generate earthquake probability maps that allowed for earthquake probability estimation. [PERSON] et al. [38] built a seismic rate prediction model for Chile by inputting earthquake data into a LSTM and convolutional neural network (CNN). The above studies were attempts to use ANNs as a tool to solve nonlinear problems in seismic engineering. Their results indicate that a suitable ANN model can surpass traditional methods to predict complex structural responses. At present, with the application of energy dissipation technology and isolation technology, the structural responses to earthquakes have strong nonlinear, diversity, and other characteristics. How to use neural networks to capture these features is still a subject worthy of further research. Inspired by the above summary, RNN and LSTM models are used to predict the response of structures with or without a nonlinear component (isolation technology) in this study.