# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 1 Introduction  
In recent years, earthquake disasters have frequently occurred, causing severe casualties and economic losses in many countries [1]. The emergency response after an earthquake is essential for saving lives; thus, a large number of studies have focused on the impact of earthquakes on structures. In order to accurately estimate the damage to building structures from earthquakes of different intensities, the dynamic response of structures under earthquake action needs to be studied. Shaking-table experimental technology can be used to test the response of structures and soil to verify their seismic performance. It is a device used to shake scaled structural models with various simulated ground motions. Time history testing using a shaking table is one of the most widely used methods to evaluate the dynamic response of structures [2]. Currently, a large number of dynamic studies using a shaking table can be found in the academic literature. For example, [PERSON] et al. [3] conducted a prototype two-story masonry building on a shaking table at the CNR-ENEA Research Center in Italy. They analyzed the behavior of the prototype from initial elasticity to the large-scale destruction of development. [PERSON] et al. [4] tested a full-scale model of a recycled aggregate block masonry building on a shaking table. The intrinsic frequency, equivalent stiffness, structuraldamping ratio, acceleration response, seismic force response, displacement response, and structural brittleness were discussed. [PERSON] et al. [5] conducted full-scale high-rise building shaking-table tests and demonstrated the validity of the numerical model used. Based on these parameters and test results, researchers were able to explain the dynamic behavior of the structure. Shaking-table tests are an important and effective method to study earthquake engineering, and they can obtain the response of a structure under a specific seismic excitation. A large number of sensors need to be arranged in the tests to record the acceleration response and displacement response of the structure. However, in shaking table tests, the sensors face the problem of missing data due to the failure of the measurement points. This means that the recorded test data do not fully reflect the structure's actual dynamic response, negatively impacting the later structural response analysis. Therefore, it is worthwhile to investigate how to complement the response of the remaining test period based on the recorded partial response data. In recent years, deep learning techniques, such as deep neural networks (DNNs), long short-term memory neural networks (LSTMs), and gated recurrent units (GRUs), have shown great potential in establishing nonlinear mapping rules between complex patterns in time series. Recurrent neural networks (RNNs) are specifically designed to learn continuous, time-varying, and linear/nonlinear patterns for regression problems [6, 7]. LSTM [8] is an improved version of recurrent neural network (RNN) architecture. It has been shown to be an appropriate tool over long time windows to identify various features of the input time history, and it has been applied to applications such as sequence labeling [9], natural language processing [10], and automatic driving [11]. Before this, much research was conducted to develop critical features for capturing time series data using deep learning models. For example, [PERSON] et al. [12] used recurrent neural network modeling with eight indicators of seismic activity as input to predict the time and location of earthquakes. [PERSON] and [PERSON] et al. [13, 14] proposed a recurrent neural network framework that can be used to classify seismic waveforms. The model achieved good results in the seismic signal classification task. [PERSON] et al. [15] used a long short-term memory neural network to model earthquake sequences, and the trained model can be used to predict future trends in earthquakes.