Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

The influence of traffic loads on the dynamic features of a bridge is an external factor that can hinder the true condition of the structure. This paper aims to effectuate a shift in the way this factor is viewed. If the interaction between vehicle and bridge is modeled using the finite element method, the response is based on mass, stiffness, and damping matrices of a coupled vehicle-bridge system that vary with the location of the load at each point in time. The time-varying forced frequencies of a beam bridge model due to a fleet of 3-axle trucks based on eigenvalue analysis (i.e., derived from the matrices of the coupled system) are compared to those obtained using dynamic transient analysis (i.e., derived from the frequency content of the acceleration response of the beam due to a truck crossing). Truck properties are randomly varied within a realistic range to obtain a pattern for the forced vibration due to a truck fleet traveling at an ideal speed of 1 m/s on a 15 m bridge with a smooth surface, and at 10 m/s on a 30 m bridge. These patterns reveal a trend that allows for locating and quantifying the stiffness loss associated with a crack using only the forced frequency. The implementation of this methodology requires the installation of accelerometers on the bridge, and a nearby weigh-in-motion system to identify the traffic fleet of interest. High requirements for frequency resolution limit the application to bridges located on low speed routes.