# The Current Development of Structural Health Monitoring for Bridges: A Review
## 6 Conclusions  
In this paper, we reviewed some noteworthy research in the field of BHM in the last five years, covering various aspects of BHM systems, especially SDI. The contributions of the various methods mentioned in this paper have been demonstrated in laboratory and practical bridge applications. However, some methods are inevitably flawed. Our main conclusions are as follows:  
1. Compared with conventional sensors, DFOS has outstanding advantages. It can continuously measure various physical quantities (strain, temperature, cracks, etc.) of the bridge along the entire length of the fiber and monitor vehicle movement information in real-time. However, two problems should be noted when using FOS. On the one hand, during on-site installation, using bare fiber will greatly reduce its service life, while on the other hand, using protective coated fiber will affect the monitoring accuracy of certain physical quantities and result in high costs. Therefore, future research may need to focus on how to improve the durability of FOS while minimizing costs. Although WSN is a good solution to the challenges of wired sensing systems, more practical problems will be encountered when choosing the best layout due to its wireless nature. Additionally, more research is expected in the future to maximize the lifespan of WSN usage. 2. For data preprocessing, effective compression methods and data augmentation methods have been developed for large amounts of data in the current research. In terms of the data noise reduction method, the introduction of ERS in railway transit has been proven to effectively reduce bridge vibration and noise problems, with promising applications. Secondly, discrete wavelet transform is more suitable for BHM than continuous wavelet transform due to the discreteness and limited nature of the actual monitoring data. Wavelet threshold denoising methods have also received attention, achieving good denoising effects by selecting the best wavelet basis and optimizing the threshold. In terms of data reconstruction, methods based on deep learning, especially those based on CNN and GAN, have high retrieval accuracy. However, research on the combination of CNN and GAN is still limited, and there is potential to develop better-performing data recovery models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Methods and/or Tools** & **Research Object** \\ \hline [PERSON] et al. (2014) & Impulse response & RC bridge decks \\ [PERSON] et al. (2015) & Acoustic emission signal & RC bridge decks \\ [PERSON] et al. (2016) & Combine visual inspection with NDT & Steel bars on bridges in Croatia \\ [PERSON] and [PERSON] (2017) & Thermal infrared imager and DL method & Steel bars on bridges \\ [PERSON] et al. (2018) & Magnetic flux detection & Bridge cable \\ [PERSON] et al. (2019) & Multibeam echosounder and sonar imaging technology & Underwater foundation of ancient stone arch \\  & & bridges in China \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Non-destructive testing methods. 3. Most research on early warning measures for bridge health is based on the dynamic response of structures. However, two case studies from Canada and England, respectively, which consider visualizing warning systems, suggest that using satellite technologies, such as InSAR, for bridge risk assessment and developing visualization platforms has great potential for application. 4. In the decades of the development of bridge damage identification methods, many systematic solutions have been proposed, such as DL-based methods, FEMU-based methods, and Bayesian inference-based methods. The increasing number of DL models make BHM more intelligent. However, to improve the accuracy of SDI, it is advisable to combine multiple methods or avoid relying solely on monitoring data. In the FEMU method, damage identification is usually achieved by observing changes in model parameters before and after damage using the established FEM.