# The Current Development of Structural Health Monitoring for Bridges: A Review
## 3 Data Processing Methods
### Data Noise Reduction  
Removing the adverse effect of noise on the signal is extremely crucial to the whole signal-processing process. In recent years, many scholars have studied new methods to deal with noise in BHM. Embedded rail systems (ERS) in rail transit can significantly reduce the impact of noise generated during traffic operation [80], and some scholars consider applying it to bridges. To popularize the application of ERS, [PERSON] et al. [81] studied the interaction behavior between ERS and its substructure. They proposed a numerical model to simulate this interaction behavior and optimize the evaluation results of the ERS-bridge interaction using a negative feedback approach. [PERSON] et al. [82] introduced the characteristics of ERS to the study. They highlight the contribution of ERS in solving vibration and noise problems that can improve the performance of existing plate girder bridges. By comparing the measured data of two plate girder bridges, it has been proved that ERS can reduce the vibration and average noise of the bridge.  
[PERSON] et al. [83] proposed a blind source separation technique based on a second-order blind recognition algorithm to reduce the impact of noise signals on bridge damage identification. They verified the accuracy of the proposed algorithm in damage frequency identification by comparing the FFT method in the numerical experiment. [PERSON] et al. [84] studied the denoising method of the dynamic deflection signal of the bridge. They first decomposed the dynamic deflection of the bridge obtained by the monitoring system into a series of intrinsic modal functions (IMF), and then removed the noisier part according to the algorithm. The remaining IMF were reconstructed as new signals, and the residual noise was further eliminated by a morphological filter method. Simulation and field experiment results show that this method has remarkable denoising ability. [PERSON] et al. [85] proposed a denoising method combining wavelet threshold denoising and Hilbert-Huang transform (HHT) to overcome the serious influence of noise on first-order natural frequency. [PERSON] et al. [86] studied the Hong Kong-Zhuhai-Macao Bridge's immersed tunnel and developed an improved wavelet threshold denoising (WTD) method to eliminate the noise in the concrete strain data. They used the sparse index and coefficient of variation to select the best wavelet basis and optimize the threshold, and finally obtained a satisfactory denoising effect. [PERSON] et al. [87] discussed the advantages and disadvantages of two denoising technologies based on discrete wavelet transform (DWT) and singular value decomposition (SVD). In both the time and frequency domains, two kinds of response signals were synthesized for testing. The results showed that both of the two denoising methods can effectively purify seismic response signals, while, for the processing of environmental vibration signals, the denoising method based on DWT exposed defects. [PERSON] et al. [88] developed a deep convolutional denoising autoencoder to reconstruct relational functions from data corrupted by noise to extract the desired features for damage recognition. [PERSON] et al. [89] introduced a generalized sidelobe canceller into the dual-sensor noise reduction method. This method realized two-stage noise reduction through a filter and determinant-based controller. Compared with other mainstream methods, the experimental results showed that the proposed method was superior.  
The wavelet threshold method is a popular and efficient signal denoising method. Figure 5 illustrates the denoising results of the monitoring data on the Hong Kong-Zhuhai-Macao Bridge's immersed tunnel using the WTD method. As shown in Figure 6, this method mainly consists of three components: signal decomposition, threshold processing,and signal reconstruction. By selecting the most suitable wavelet base, decomposition level, threshold value, and threshold function, the noise reduction effect can be improved.