Abstract: Cable prestress is a critical parameter for finite-element modeling of cable-net structures. Due to the strong coupling among cables in large-scale cable-net systems, conventional modal-based methods have difficulty in accurately measuring prestress and suffer from low identification efficiency. A multi-experiment sensitivity-based prestress correction algorithm was proposed. Compared with single-case identification, multiple experiments were used to enrich sensitivity information, break near collinearity among sensitivity columns, alleviate rank deficiency and improve identification accuracy, while noting that it may also introduce redundancy in the sensitivity matrix, increase the computational burden, or lead to noise amplification under ill-conditioned sensitivity matrix. Photogrammetric measurements were employed to obtain measured nodal displacements, and the correction objective was defined as the minimization of the RMS error between finite-element computed displacements and experimental displacements. Sensitivity matrices were constructed by finite differences under multiple loading cases and assembled for identifiability analysis. A parameter-aware Tikhonov regularization was introduced to suppress ill-posed amplification and out-of-bound updates, and Leave-one-out cross-validation (LOOCV) was used to adaptively adjust the update step size. The proposed algorithm was applied to two test cases: a single planar segment of a radial-rib cable net antenna and a complex three-dimensional spatial cable net. After five iterative corrections, the prestress estimation accuracy was improved by 96.73% for the planar segment and by 85.74% for the complex spatial net, respectively. The method achieves high accuracy and provides a reliable tool for online prestress calibration of large-scale cable-net structures.

Key words: cable-net antenna, photogrammetry, finite-difference method, model updating, prestress correction