# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 1 Introduction
The results were compared with ordinary feedforward neural network solutions, showing that the LSTM neural network had better performance. [PERSON] et al. [16] developed an LSTM model for predicting building response based on ground motion records and structural properties. It was shown that the LSTM network was a reliable and computationally efficient method for predicting nonlinear structural response. [PERSON] et al. [17] combined recurrent neural networks with Bayesian training for the response prediction of large buildings. The proposed method exhibited excellent results in actual conditions, with the advantage of low computational cost. [PERSON] et al. [18] proposed an adaptive algorithm that identifies the optimal number and location of sensors within a given urban area and uses deep neural networks to estimate regional structural damage given a spatial distribution of earthquake intensity levels. [PERSON] et al. [19] generated time-frequency distribution maps of ground shaking as a detailed visual representation of the frequency domain features and time-domain features of ground shaking. These data were then used to train convolutional neural network CNN models, which could predict damage states effectively. Research on hybrid models with deep learning methods to complement missing data has gradually increased. [PERSON] et al. [20] combined empirical modal decomposition with a long and short-term memory deep learning network to recover the measured signal data. This hybrid model transformed the missing data prediction work into a time series prediction work, which was then solved using a partitioning strategy. [PERSON] et al. [21] proposed a baseline correction method based on EMD-DNN, which used deep neural networks (DNNs) to extract features from the multiple time series obtained by EMD based on removing the drifting trend with empirical modal decomposition (EMD) and, finally, achieved the prediction of the actual displacement. [PERSON] et al. [20] proposed an EMDBiGRU model for missing data prediction. The proposed model could better capture the microscopic changes in the measured data and exhibited better prediction performance than the pure machine learning algorithm. However, there are two problems in using EMD to decompose the measured signal data: first, the IMF obtained by decomposition with EMD suffers from modal mixing, which results in inaccurate IMF components; second, the end effect affects the decomposition effect. [PERSON] et al. [22] proposed an improved EMD method based on a noise-assisted analysis called ensemble empirical mode decomposition (EEMD), using EMD to analyze the results after white noise decomposition. EEMD can effectively suppress the modal mixing phenomenon of EMD and has a better decomposition effect. From the above studies, it is clear that deep learning methods (i.e., DNN, GRU, and LSTM) have attracted the attention of many researchers and have been widely used in the field of earthquake engineering. These previous studies confirm the utility and success of employing RNN (or LSTM) to capture various ground motion features and approximate the response of various building types. A common implication of all these prior studies is that alternative models can establish an accurate mapping between the recorded time-history data and the structural response. The goal of forming mappings between complex patterns of sensor time series and missing response data follows similar thinking. However, few models have been used to address the under-recorded sensor data in earthquake engineering. In addition, although shaking-table tests are used as an effective method to obtain the acceleration and displacement responses of structures under seismic excitation, they also face the problem of missing data due to the failure of sensors, which has an impact on structural response analysis.