A vision-based method for estimating shallow cable tension via vibrational properties

Shallow cable is the component of cable-supported glass roof systems, which has been widely used in modern building constructions. In this research, a vision-based method is proposed for estimating tension values of shallow cables subjected to harmonic excitations. To validate the accuracy of the proposed low-cost, easy-to-operate, and multipoint synchronous measurement method, vision-based results are compared with the data obtained from contact sensors, and the error is within 2%. In addition, the data-driven stochastic subspace identification (Data-SSI) is employed to identify the first-order modal shapes of the shallow cable by vision-based data, and the results have a precise match with the computed shape of the numerical analysis. To investigate the practicality of the proposed method, the uncertainty evaluation for shallow cable tensions obtained by vision-based data is conducted based on the Monte Carlo method (MCM). These results demonstrate that the method proposed in this research is effective for capturing vibrational properties and estimating shallow cable tension.