Masts are frequently positioned on the top of super high-rise structures to provide lightning protection， collision prevention， and enhanced architectural aesthetics and height. Being highly sensitive to wind， these structures are susceptible to fatigue-induced damage due to wind-induced vibrations. In May 2021， an abnormal vibration incident at a building in Shenzhen attracted significant global attention. Determining the cause of fatigue damage in the mast became one of the key challenges in tracing the source of this incident.However，due to a lack of comprehensive wind field historical data， the load-structure response analysis method could not be directly applied to address the wind-induced fatigue issue of masts. In practice， one or two cameras are typically installed on the top of super high-rise buildings to monitor the service status of masts， offering foundational data for fatigue life assessment based on image information. However， due to variations in site conditions， the image data regarding the masts’ service status often exhibit spatiotemporal incompleteness， complicating the direct acquisition of historical wind vibration data through image analysis. For this purpose， this study introduced a method for the inverse analysis of the wind-induced vibration history of mast structures using incomplete image data. By examining the temporal and frequency domain characteristics of the limited image data， conducting damage assessments at key joints， and analyzing the natural vibration properties of the mast structure， the probability distribution of stress states in fatigue-sensitive joints was derived. This was complemented by a stochastic sampling algorithm to achieve a fatigue reliability evaluation of such structures. Experimental results indicated that the assessment outcomes of this method were highly consistent.
Shi et al. (Thu,) studied this question.