# The Current Development of Structural Health Monitoring for Bridges: A Review
## 3 Data Processing Methods
### Data Reconstruction  
In a typical BHM system, data are often missing due to sensor failures, or data are incomplete due to sensor signal loss during transmission. When the data loss rate is too high, the health assessment of the structure cannot be carried out properly, which may lead to serious consequences. To ensure the reliability of the monitoring data used for analysis, many studies have been carried out. Due to its excellent ability in processing large amounts of data and extracting features, DL has been applied in various fields such as medical treatment, computer vision, finance, and transportation [90; 91; 92; 93]. [PERSON] et al. [94] proposed a data recovery framework based on a deep neural network to recover long-term missing wind data from bridges. The framework divides data recovery into two tasks and takes advantage of a free access database in Europe to obtain wind data for learning. In addition, they used a time-frequency cross-domain loss function for training to enhance the reconstruction performance of the wind speed signals. In the case study of Sutong Bridge in China, the feasibility and effectiveness of the proposed framework were verified. The convolutional neural network (CNN), as a representative algorithm in DL, has become a research hotspot in the field of SHM recently. [PERSON] et al. [95] developed a new CNN structure to recover lost vibration data in SHM. They introduced the bottleneck architecture and skip connection in a CNN to construct the nonlinear relationship between  
Figure 5: Time series of the monitoring data before and after WTD [86]. Figure 6: Process of WTD (Ca: the low-frequency information; Cd: the high-frequency information). the damaged data signal and the intact signal. The effectiveness and robustness of the proposed method were verified by comparing the measured data of the pedestrian bridge. Furthermore, they studied the case of a high data loss rate, and the proposed method still showed excellent data recovery ability. [PERSON] et al. [96] studied a training method for CNN. They first used healthy monitoring data to build a CNN model. They then deliberately excluded responses from specific sensors and used the response data from the remaining sensors as input to the neural network. Through numerical and experimental studies, it was verified that the trained CNN can effectively recover the excluded sensor data. [PERSON] et al. [97] reported a neural semantic recovery framework that transformed data recovery into a conditional probability modeling problem. They used the semantic features of the vibration data as conditions and the CNN as a feature capture tool. In addition, they proposed a new perceptual damage function to improve the efficiency of the network. The results of the case study showed that the framework had splendid data recovery accuracy even when the loss rate was high. In 2014, inspired by game theory, generative adversarial networks (GAN) were developed to generate high-resolution images [98]. As one of the most successful generative models in DL, GAN has developed many variants over the years and has been used in the field of SHM. [PERSON] et al. [99] proposed a missing data interpolation method based on the non-supervision GAN framework to recover the missing deflection data of BHM systems. They used a thin neural network and a generator-discriminator structure to form the data recovery framework. By analyzing the measured data of a highway and railway bridge, the advantages of the proposed method over the traditional GAN model in execution speed and reconstruction accuracy were verified. [PERSON] et al. [100] introduced the Bayesian dynamic regression method to improve the GAN model. They reported that the method can achieve the ability to model the collected data set and make missing predictions. The validity of the model was verified by using the engineering data of Jiangdong Bridge in Hangzhou. [PERSON] et al.