# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 1 Introduction
[PERSON] et al. [26] used Lebesgue sampling parallel state fusion LSTM (LS-PSF-LSTM) to predict a lithium-ion battery early-cycle stage RUL; the experiments demonstrated the effectiveness of the proposed method. [PERSON] et al. [27] proposed a CNN-LSTM deep-learning framework to consider the spatial-temporal correlations together with the extrinsic features for flight delay prediction. Remarkably, their methodology achieved an accuracy of 92.39%, thereby demonstrating its tremendous potential. [PERSON] et al. [28] coupled LSTM to principal component analysis (PCA) and moving average (MA), respectively, and proposed that the coupling prediction model is better. [PERSON] et al. [29] proposed a method that combined a multi-Gaussian fitting feature extraction method with an LSTM-based damage identification method to develop a health monitoring system with available vibration signals, which can significantly reduce computation time in the condition of achieving great prediction accuracy. With the continuous exploration of long-term and short-term memory networks by scholars and experts, more and more researchers put forward the innovation of the LSTM network model and training methods and are committed to improving the training accuracy and convergence speed of the model. Although LSTM has a strong fitting effect on long-term time-series prediction, it displays shortcomings in predictions with low sample data, and the prediction effect is not satisfactory. The support vector regression (SVR) learning algorithm is based on the principle of structural risk minimization with excellent small sample prediction performance. This method has been successfully applied in numerous prediction fields and provides unique advantages. [PERSON] et al. [30] presents a comprehensive prediction model that combines time-series decomposition, the least square method, sparrow search algorithm (SSA), and support vector regression (SVR). The effectiveness of this model was evaluated using monitoring data from E13-E14 and E17-E18 joints, opening-closing deformation, of an immersed tunnel in the Hong Kong-Zhuhai-Macao Bridge. [PERSON] et al. [31] proposed a non-intrusive load decomposition method based on CNN + SVR substation, which can provide load composition information of different degrees of fine grain and can better understand the power information of the industry and users of the substation. [PERSON] et al. [32] analyzed different algorithms (BPNN, SVR, RF, LSTM, LSTM-SVR) to perform multivariate non-Gaussian wind pressure conditional simulation with spatial interpolation prediction, and their prediction performance measures (MAPE, RMSE, R) are compared. The results indicate that the combined algorithm (LSTM-SVR) can utilize a small amount of data to realize the multivariate non-Gaussian conditional simulation with spatial interpolation prediction more accurately. [PERSON] et al. [33] presented a hybrid prediction model based on ARIMA and SVR, and the experimental results showed that the mixed model had high prediction accuracy and could accurately describe the complex change trend of the time series of the number of borrowers. Although LSTM-SVR has been widely used in various fields and has achieved great achievements, there is little research in the field of structural damage monitoring. In this sense, we must find a high-precision and relatively practical prediction method according to the rules and characteristics of structural vibration state changes. In this paper, the VMD decomposition method and the LSTM-SVR integrated model are combined and applied to structural damage prediction, ultimately achieving an improvement in the overall algorithm prediction performance. It is of great significance to predict the damage trend of structures when there is less engineering vibration data in the future.