Computer vision‐based real‐time cable tension estimation algorithm using complexity pursuit from video and its application in Fred‐Hartman cable‐stayed bridge.

Summary: Real‐time health monitoring of stay cables in cable‐stayed bridges is necessary for timely maintenance and to avoid unforeseen fatigue damage due to vortex‐induced vibration—mainly due to combination rain and wind‐related dynamic loads. Conventional contact‐based sensors may often malfunction in harsh weather conditions and are expensive to install and maintain. Therefore, recently, the usage of non‐contact camera‐based measurement is burgeoning in the domain of structural sensing. Non‐contact video‐based sensing provides a higher spatial resolution compared to conventional sensors along with a lower cost. Therefore, in this paper, we present a framework that uses video‐based measurement as multiple sensors to reduce the estimation error in determining the real‐time cable tension. First, we calculate the vibration response using the phase‐based motion estimation algorithm for various locations of interest. We then intuitively fuse the data from all the locations to estimate the real‐time frequency variation using a blind source separation (BSS) technique named complexity pursuit (CP). Finally, the real‐time stay‐cable tension is calculated from the real‐time frequency history using the taut‐string theory. The proposed algorithm is applied to Fred‐Hartman cable‐stayed bridge in Houston, Texas. The algorithm is validated using actual tension in the cable. We also show that the estimation error in the proposed sliding window‐based CP framework is considerably lesser than the conventional real‐time tension estimation technique using Short‐time Fourier Transform (STFT). The accurate estimation of stay‐cable tension from the video‐based measurement shows the significant potential of the proposed framework in the domain of structural health monitoring. [ABSTRACT FROM AUTHOR]