Structural health monitoring (SHM) technology for surveillance and evaluation of existing and newly built long-span bridges has been widely developed, and the significance of the technique has been recognized by many administrative authorities. The paper reviews the recent progress of the SHM technology that has been applied to long-span bridges. The deployment of a SHM system is introduced. Subsequently, the data analysis and condition assessment including techniques on modal identification, methods on signal processing, and damage identification were reviewed and summarized. A case study about a SHM system of a long-span arch bridge (the Jiubao bridge in China) was systematically incorporated in each part to advance our understanding of deployment and investigation of a SHM system for long-span arch bridges. The applications of SHM systems of long-span arch bridge were also introduced. From the illustrations, the challenges and future trends for development a SHM system were concluded.

Graphene oxide (GO) has been widely used as an additive due to its numerous unique properties. In this study, the compressive strength, flexural strength and elasticity modulus of concrete containing 0.02 wt%, 0.05 wt % and 0.08 wt % GO, and its dry shrinkage performance have been experimentally investigated. After the sample preparation, apparatus for compression test and flexural test were used to test the relevant properties of concrete containing GO. The dial indicators were used to measure the shrinkage of samples. The results indicate that GO can considerably improve the compressive strength, flexural strength, and elasticity modulus of concrete at the concrete age of 28 days by 4.04–12.65%, 3.8–7.38%, and 3.92–10.97%, respectively, which are substantially smaller than the increment at the age of 3 d by 5.02–21.51%, 4.25–13.06%, and 6.07–27.45% under a water-cement ratio of 0.35. It was also found that GO can increase the shrinkage strain of concrete. For example, at the age of 60 days, 0.02 wt%, 0.05 wt% and 0.08 wt% GO can increase the shrinkage strain of ordinary concrete by 1.99%, 5.79% and 7.45% respectively under a water-cement ratio of 0.49. The study has advanced our understanding on mechanical and shrinkage behavior of concrete containing GO.

Bridges are an important component of transportation. Flutter is a self-excited, large amplitude vibration, which may lead to collapse of bridges. It must be understood and avoided. This paper takes the Jianghai Channel Bridge, which is a significant part of the Hong Kong-Zhuhai-Macao Bridge, as an example to investigate the flutter of the bridge deck. Firstly, aerodynamic force models for flutter of bridges were introduced. Then, wind tunnel tests of the bridge deck during the construction and the operation stages, under different wind attack angles and wind velocities, were carried out using a high frequency base balance (HFBB) system and laser displacement sensors. From the tests, the static aerodynamic forces and flutter derivatives of the bridge deck were observed. Correspondingly, the critical flutter wind speeds of the bridge deck were determined based on the derivatives, and they are compared with the directly measured flutter speeds. Results show that the observed derivatives are reasonable and applicable. Furthermore, the critical wind speeds in the operation stage is smaller than those in the construction stage. Besides, the flutter instabilities of the bridge in the construction and the operation stages are good. This study helps guarantee the design and the construction of the Jianghai Channel Bridge, and advances the understanding of flutter of long afterbody bridge decks.

Even though large‐scale projects, such as sea‐crossing bridges and tunnels, have complex structures and service conditions, the structural health monitoring (SHM) system can comprehensively monitor the stress situation and damage evolution law in the entire construction and service process of such structures. Based on the background of the Hong Kong–Zhuhai–Macao Bridge, this study systematically introduces the overall goal and framework of the SHM system, monitoring content, and sensor information. Moreover, the structural health condition evaluation methods and data analysis methods are presented in detail. Then, on the basis of a multilayer data storage system, a fault‐tolerant data center platform design, an open integrated supervision platform design, and other measures, a set of reliable and advanced large‐scale software systems are built to achieve the SHM system for the Hong Kong–Zhuhai–Macao Bridge. Finally, the wind characteristics around the Hong Kong–Zhuhai–Macao Bridge and some of the monitored structural responses from the Super Typhoon Mangkhut in 2018 are shown, which verified that the SHM system can accurately and reliably monitor and feedback the environmental load and structural response of the principal parts of the Hong Kong–Zhuhai–Macao Bridge under complicated service environment.

Accurate deformation prediction is crucial for ensuring the safety and longevity of bridges. However, the complex fluctuations of deformation pose a challenge to achieving this goal. To improve the prediction accuracy, a bridge deformation prediction method based on a bidirectional gated recurrent unit (BiGRU) neural network and error correction is proposed. Firstly, the BiGRU model is employed to predict deformation data, which aims to enhance the modeling capability of the GRU network for time-series data through its bidirectional structure. Then, to extract the valuable information concealed in the error, a transformer model is introduced to rectify the error sequence. Finally, the preliminary and error prediction results are integrated to yield high-precision deformation prediction results. Two deformation datasets collected from an actual bridge health monitoring system are utilized as examples to verify the effectiveness of the proposed method. The results show that the proposed method outperforms the comparison model in terms of prediction accuracy, robustness, and generalization ability, with the predicted deformation results being closer to the actual results. Notably, the error-corrected model exhibits significantly improved evaluation metrics compared to the single model. The research findings herein offer a scientific foundation for bridges’ early safety warning and health monitoring. Additionally, they hold significant relevance for developing time-series prediction models based on deep learning.

It is essential to construct structural health monitoring systems for large important bridges. Zhijiang Bridge is a cable-stayed bridge that was built recently over the Hangzhou Qiantang River (the largest river in Zhejiang Province). The length of Zhijiang Bridge is 478 m, which comprises an arched twin-tower space and a twin-cable plane structure. As an example, the present study describes the integrated system of structural health monitoring and intelligent management for Zhijiang Bridge, which comprises an information acquisition system, data management system, evaluation and decision-making system, and application service system. The monitoring components include the working environment of the bridge and various factors that affect bridge safety, such as the stress and strain of the main bridge structure, vibration, cable force, temperature, and wind speed. In addition, the integrated system includes a forecasting and decision-making module for real-time online evaluation, which provides warnings and makes decisions based on the monitoring information. From this, the monitoring information, evaluation results, maintenance decisions, and warning information can be input simultaneously into the bridge monitoring center and traffic emergency center to share the monitoring data, thereby facilitating evaluations and decision making using the system.