Deformation monitoring and dynamic characteristic analysis of bridge structures are the vital and basic requirements for the safe operation of bridges. In recent years, Global Navigation Satellite System (GNSS) has become increasingly widely used in bridge structural health monitoring with the development of the GNSS technology, especially the continuous improvement and development of China's Beidou navigation satellite system (BDS). This article summarizes the application process of GNSS dynamic deformation monitoring and the development of GNSS deformation measurement technology of bridge structural health monitoring, the dynamic characteristic identification method and its application in bridge GNSS monitoring. The positioning solution methods for GNSS monitoring, the high sampling rate GNSS receiver for monitoring, multi-frequency and multi-system GNSS monitoring and the weakening of multipath effect of GNSS monitoring are summarized in detail. Then, the conclusions and prospects are posed for future research and related application.

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection.

In recent years, global positioning system (GPS) has been widely used for the measurement of deflections of bridges. However, due to multipath and satellite signal obstructions caused by towers, cables, and passing vehicles, the reliability of deformation monitoring with GPS is still a problem. Recent research with respect to multi–global navigation satellite system (multi-GNSS) technology, though, has enhanced satellite visibility and availability for positioning, navigation, and timing (PNT) for users. Its benefits involving application in bridge monitoring are still rarely studied. In this paper, we propose a composite strategy where integrated GPS and BeiDou navigation satellite system (BDS) dual-frequency, carrier-phase data processing is carried out to improve the reliability of bridge monitoring with GNSS measurements. In addition, signal-to-noise ratio (SNR)–based stochastic model and postfit residual editing strategies are utilized to enhance the reliability further. In a group of fixed-point experiments, improvements of 20–30% in precision were achieved with the integrated GPS and BDS compared to GPS-only results. Based on the real GPS and BDS measurements collected on the Baishazhou Yangtze River Bridge in China, we assessed the performance of the proposed method. In the vibration experiment, no apparent effects on natural frequency identification were found by introducing BDS into the solution in an ideal observation environment. However, the combined GPS and BDS results seemed to be much more promising, with lower background noise. Meanwhile, the integrated GPS and BDS data processing with postfit residual editing and SNR-based stochastic model strategies effectively managed satellite signal obstruction and the influence of multipath effect to attain reliable dynamic deformation-monitoring information for bridges.