Computer vision‐based real‐time cable tension estimation in Dubrovnik cable‐stayed bridge using moving handheld video camera

Abstract

Summary Cables are essential components of the cable‐stayed bridges as they serve as the main load‐bearing component. Hence, continuous monitoring of such cables becomes necessary as they are vulnerable to the fatigue damage induced by dynamic loads. Sensors are attached to the cables to examine the health of the cables; however, these contact‐based sensors can malfunction in harsh weather condition, which makes impossible to estimate the cable health in such unfavorable condition. Therefore, in this paper, we propose a completely noncontact video‐based stay‐cable tension measurement technique where the video is recorded using a moving handheld camera at a significant distance from the structure itself. Here, the cable tension is determined from vibration‐based measurement, but the vibration of the cable recorded in the video includes the true vibration of the cable along with the camera motion. Hence, we amalgamated a series of image processing techniques to nullify the camera movement. First, we detect the camera movement based on the movement of the bridge deck and pylon, which are fixed objects, using Kanade–Lucas–Tomasi (KLT) feature tracking algorithm. Then we nullify the camera movement by using the affine transformation matrix obtained by random sample consensus (RANSAC) algorithm. Subsequently from the steady video, the cable motions are estimated using the phase‐based motion estimation technique. From the time history of the cable vibration, real‐time frequency variations are estimated using Short‐Time Fourier Transform (STFT). Finally, the real‐time tension is determined from this dominant frequency variation history using the taut‐string theory. This paper shows the significant potential of camera‐based sensing techniques in structural health monitoring as the mean estimated tension and the design cable tension are found to be comparable.

Structural engineering from an inverse problems perspective

“…Similar efforts have been reported in [192,193,194]. Furthermore, through the usage of cameras as displacement sensors, other key structural features, such as natural frequencies/mode shapes [195,196], beam influence lines [197], and bridge cable loads [198] have been be estimated.…”

Structural engineering from an inverse problems perspective

“…Similar efforts have been reported in [198,199]. Furthermore, through the use of cameras as displacement sensors, other key structural features, such as natural frequencies/mode shapes [200,201], beam influence lines [202] and bridge cable loads [203], have been be estimated.…”

Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

“…In addition, the dominating vibrational mode of cable was extracted using the Hankel dynamic mode decomposition method in their further research. 5 Jana and Nagarajaiah 6 proposed a noncontact cable force measurement approach using a moving handheld camera and nullified the camera movement based on a series of image processing techniques. Kim et al 7 developed a vision-based system equipped with a pan-tilt drive for tension force estimation of multiple cables.…”