# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
###### Abstract
It indicated that the reconstruction quality of the signal was affected by the noise level of the signal and the missing data rate. [PERSON] [9] proposed a combination of a state-space strategy and singular value decomposition to reconstruct multivariate time series collected from structural health systems. This model can effectively determine the correlation between multivariate variables and fill in missing data. [PERSON] [10] reconstructed SHM data by analyzing the different covariance functions between the collected response data and proposed multi-task learning based on Bayesian theory. The experimental results showed that the method has high accuracy compared with traditional methods. [PERSON] [11] studied the reconstruction problem of randomly lost vibration signals and pointed out that single-channel acceleration signals had the features of general frequency domain sparsity while the matrices composed of multi-channel acceleration signals were low-rank in singular value decomposition. Based on this, he proposed a method based on the \(\ell 1\) parametric constraint-based optimal sparse signal reconstruction and the method of the nuclear parametric constraint-based optimal low-rank matrix complementation. Another type of method attempted to use estimated values as missing data replacements, which is called interpolation in statistics. Considering that data interpolation has long been used as a common means of resolving missing data, many research scholars have conducted extensive studies on this area and proposed interpolation methods, such as using spatial and temporal correlation to fill in missing values, autoregressive moving average models, machine learning and other methods. [PERSON] [12] studied the problem of lost strain data in the construction monitoring of mega-columns. In the paper, an analytical relationship equation for strain monitoring data between different measurement points in the same cross-section of a mega-column was derived, and the lost axial strain data were repaired accordingly. He [13] defined the missing data problem for critical locations of structures without installed sensors and then proposed a hybrid model based on finite element modeling and empirical model decomposition to solve this problem. It indicated that the proposed method is capable of recovering the missing data from a single-channel acceleration signal. that the proposed method could reconstruct the data directly in the time domain with high accuracy. Considering that sensors have non-linear phase components, [PERSON] [13] proposed a non-parametric joint distribution method to handle the missing data, considering the correlation between different sensor strains. [PERSON] [14] studied the loss mechanism of steel construction monitoring data and classified the causes of its monitoring missing data into three categories: data transmission failure, monitoring system power failure, and monitoring equipment failure. Based on this, he proposed a missing data compensation method based on linear regression. [PERSON] [15] adopted relative information of stress data to recover the lost stress data from the steel construction monitoring of Hangzhou Olympic Center by using the linear regression model. Because the data collected by SHM is non-linear and non-stationary, the traditional method is weak in handling non-linear data. With the rapid development of artificial intelligence, deep learning methods have been used to recover the missing data, which has received extensive attention from the academic community. Since sensor data collected from SHM belong to time-sequence data, existing deep learning methods such as long short-term memory (LSTM) [16], bidirectional gated recurrent unit (Bi-GRU) [17], gated recurrent unit (GRU) [18], and convolutional network [19] have an excellent ability in handling time-sequence data and can effectively recover missing data. Such as, [PERSON] [20] proposed a data recovery method based on an LSTM deep learning algorithm to recover missing data of structural temperature.