# The Current Development of Structural Health Monitoring for Bridges: A Review
## 3 Data Processing Methods
### Data Preprocessing  
Under the influence of the uncertainty of the BHM monitoring environment, sometimes the data collected on site are unbalanced and inadequate, which leads to large errors in the bridge health assessment results, resulting in huge losses [73,74]. Therefore, to make the subsequent evaluation more accurate, it is necessary to preprocess the data collected by sensors. In 2019, [PERSON] et al. [75] proposed a framework based on a multi-source fusion positioning system. In this framework, a big sensor data preprocessing (BSDP) scheme, including extraction, acquisition, and transmission, is proposed to solve the problem of large amounts of data. Multi-source sensor data sources include WiFi, fingerprint, accelerometer, gyroscope, magnetometer, etc. To improve the efficiency of data transmission, they used compression sensing technology to compress the data. Experimental and simulation results demonstrated the effectiveness of this BSDP scheme. In 2021, [PERSON] et al. [76] proposed a data enhancement model based on the generative adversarial nets to expand the existing monitoring data of bridges. They reported that the model generated new monitoring data through learning coupling among bridge monitoring factors. The simulation results showed that the bridge monitoring data generated by the model was real and effective, through which the performance of the bridge health assessment was improved. In a study on the compression and smoothing of data streams, [PERSON] et al. [77] introduced an algorithm with an adaptive search space and a special space reduction technique, aiming to provide an efficient method for processing sensor data in Internet of Things devices, health monitoring systems, autonomous vehicles, and robots. The numerical results showed that the proposed algorithm had lower errors and higher compression ratios than the reference algorithm.  
When cracks or even fractures appear in materials, local energy quickly concentrates, and when it reaches a threshold, acoustic emission (AE) signals can be generated. By analyzing the characteristic parameters of AE signals, the state of the material can be determined. [PERSON] et al. [78] successfully used deep neural networks to construct the relationship between the scalograms of AE signals and the state of bridge cables. In addition, the experimental results demonstrate that, as two kinds of sensors with different resonant frequencies, the R6I-AST type of sensors are more suitable for detecting the break inside the cable than the R3I-AST type of sensors. [PERSON] et al. [79], in a study of the acoustic emission signals of three bridges, proposed an acoustic emission signal segmentation algorithm to solve the problem that BHM data occupied a large amount of data storage space. The analysis results showedthat the algorithm can not only effectively save the data storage space, but also accurately extract the noise signal to determine the filtering threshold.  
Regarding the preprocessing methods for monitoring data, three points can be summarized: (i) we can compress the raw data to improve the transmission efficiency; (ii) we can also expand the monitoring dataset through data generation models to provide more evidence for bridge health assessment; (iii) establishing the relationship between monitoring data and structural status is always beneficial for the next step of assessment.