Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

The Sutong Bridge with a span of 1088 m is supported by 272 cables, whose lengths range from 152.85 m to 576.77 m (the longest in the world). The half-year vibration acceleration data of two cables from structural health monitoring system (SHMS) is analysed, in conjunction with the wind field data, weather data and vehicle data to explore and three types of cable vibrations. The large-scale cable vibration is mainly rain-wind-induced vibrations (RWIV) when the wind direction lies in an angular range of 40°–80° relative to the cable axis, and the bridge-deck wind velocity is 4–20 m/s. The ultra-long cable also experiences in-plane vibrations, which satisfy many characteristics of vortex-induced vibrations (VIVs). Cable VIV occurs only when the bridge-deck wind velocity 4–8 m/s, with the frequency ranging from 9.5 Hz to 10 Hz. The daily small-amplitude vibration of the cables and steel girder is mainly induced by the passage of heavy trucks. There exists a positive correlation between the number of heavy trucks and the vibration level. Based on the characteristics and occurrence probabilities of these three types of cable vibrations, corresponding measures are suggested for the maintenance of ultra-long cables.