Dapeng Zhu

Deformation monitoring of ultra‐high pylons in super‐long‐span cable‐supported bridges faces considerable challenges due to structural complexity, stringent installation accuracy requirements, and susceptibility to environmental interference when using conventional methods. This paper presents the development and validation of a real‐time deformation monitoring method for ultra‐high pylons based on machine vision technology, with the Zhangjinggao Yangtze River Bridge serving as a case study. High‐resolution industrial cameras form the core hardware of the system, which are equipped with specially designed retroreflective targets and integrated with environmental humidity and temperature sensors. The system implements Zhang’s camera calibration method and subpixel localization algorithms. The core contributions of this study are: (1) the first high‐precision monitoring over a distance of 300 m during the active construction of a 350 m ultra‐high pylon; (2) a hybrid subpixel localization algorithm (0.1‐pixel precision) robust to varying illumination conditions; and (3) a complete uncertainty quantification framework incorporating atmospheric turbulence and thermal drift. Field test results demonstrate a measurement accuracy better than 2 mm for the south pylon under all tested conditions. The average measurement errors were 0.25 mm during the daytime and 0.15 mm at night. The system successfully detected cyclical deformations caused by temperature variations and captured transient responses induced by construction activities, with a peak deformation fluctuation of 3.28 mm. Quantitative agreement with theoretical models is confirmed (mean absolute difference ≤ 0.35 mm, R ² ≥ 0.94). The proposed methodology offers significant improvements in construction safety, quality control, and demonstrates broad applicability, holding promise for widespread adoption in engineering practice.

Presently, foundation pit support structures are generally regarded as the temporary structures and the impact of vibration loads is often overlooked. As opposed to static and seismic loads, the vibration loads of subway trains are a type of cyclic load with a relatively long duration of action and a definite cycle; it is of great importance for the design of foundation pit support structures to correctly evaluate the impact of subway train vibrations on deep foundation pit and support works. In this paper, a dynamic three-dimensional numerical model is built that considers the vibration load of subway trains on the basis of the static numerical model for deep foundation pit support structures and simplified train loads to study the impact of train vibrations on deep foundation pit and permanent support structures. Studies have shown that the dynamic response of surface displacement mainly occurs in the early period of dynamic load, the vibration load of subway trains has little impact on ground subsidence, the support pile structure is in an elastic state during dynamic response under the action of subway train vibrations, and the action of train vibration loads is inimical to the safety of foundation pit support structures and should be closely studied.