Accurate force evaluation in prestressed cable-strut structures: A robust sparse Bayesian learning method with feedback-driven error optimization

Abstract

Force evaluation is critical to ensuring the safety of cable-strut structures during service. This study employs dynamic testing to assess the internal forces resulting from cable relaxation in prestressed cable-strut structures. A cross-model cross-mode algorithm is utilized to establish a cable force evaluation model. This approach broadens the range of available modes and addresses mismatches between modes before and after cable force loss. To enhance the accuracy and reliability of the force evaluation, a robust sparse Bayesian learning method is proposed. Measurement noise is modeled as a mixture of Gaussian distributions rather than a single Gaussian distribution, enabling a more precise representation of uncertainties in force evaluation. Furthermore, a feedback-driven error optimization process is introduced to minimize residuals through multiple linear iterations. Numerical simulations demonstrate that the proposed method achieves greater evaluation accuracy compared to existing sparse Bayesian approaches. Comparative analyses under varying noise levels reveal that the proposed method is robust and effectively reduces the impact of measurement noise.