# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
###### Abstract
Then, he discussed in detail the application of LSTM in structural temperature missing data recovery on the Yangtze River Bridge. It indicated that the proposed method has higher accuracy in predicting missing data than support vector and wavelet neural networks for temperature data. [PERSON] [21] proposed a method that used CNN to recover missing vibration data in the SHM system. Firstly, he used CNN deep algorithm to extract the non-linear relationship between the missing and real data. Then, CNN was used to extract features of the incomplete data through the compression layer and gradually extended these features in the reconstruction layer to recover the missing data. [PERSON] [22; 23] proposed an advanced metaheuristic optimization, called aquila optimizer, which is powerful in tackling missing data. In the above algorithms, the BiGRU model is a data-driven method that does not need many assumptions and can automatically adjust its parameters according to the data. More importantly, the BiGRU can extract the pre-post correlations of subsequence, which can be employed for capturing complex state changes. Moreover, to gain a better understanding of the variation of measured data, empirical mode decomposition (EMD) can be used to decompose signal data before BiGRU is applied. EMD is a self-adaptive analysis method that can capture a set of intrinsic mode functions (IMFs) [24]. [PERSON] [25] used the EMD to recognize damaged locations in beams and made an FE simulation of a steel beam to verify the effectiveness of the proposed method. The results showed that IMFs represent the different patterns of the raw measured signal. [PERSON] [26] decomposed the original power signals into different load components via EMD measured and fed them into a neural network model. It indicated that the proposed method could effectively improve the accuracy of predicted ability. He [13] used the EMD method to decompose the dynamic response data collected by SHM and solved the dynamic response at critical locations using the model superposition method. The experimental results on the finite element beam model showed that the proposed method has high accuracy. In recognition of the strong abilities of EMD to decompose measured signal data and BiGRU to capture the pre-post correlations of subsequence, an EMD-BiGRU model is proposed in this study for the imputation of missing measured signal data. Moreover, shallow neural networks are also used as prediction models, which are used to verify the proposed EMD-BiGRU. Finally, a three-story building structure from LOS ALAMOS national laboratory is used to evaluate the performance of EMD-BiGRU [27]. The three-story building structure is commonly used to evaluate machine learning-based SHM. The main contributions of this paper are summarized as follows. (1) A novel sensor data-driven data loss recovery technique is proposed by combining EMD with BiGRU, which can directly tackle data recovery problems and accurately recover the missing data. (2) The comprehensive experiments based on a three-story building structure are used to explore the effectiveness of the proposed method. The results indicate that the proposed method achieves a high accuracy under different missing lengths. Meanwhile, several existing loss recovery techniques built on machine learning algorithms are selected for analysis, and the results demonstrate the effectiveness and superiority of the EMD-BiGRU. (3) The data loss recovery of the entire framework is a data-driven method, and it is not dependent on some assumptions of the data and the FE model. The remaining sections of this paper are organized as follows: Section 2 introduces the theoretical background of the EMD-BiGRU. Section 3 describes the experimental setting, including data sets and parameters of different algorithms. Section 4 analyzes the experimental results of four data sets using the proposed method and traditional method. Section 5 summarizes some conclusions based on EMD-BiGRU and potential topics for future research.