Compressive sampling based approach for identification of moving loads distribution on cable-stayed bridges

Abstract

A moving loads distribution identification method for cable-stayed bridges based on compressive sampling (CS) technique is proposed. CS is a technique for obtaining sparse signal representations to underdetermined linear measurement equations. In this paper, CS is employed to localize moving loads of cable-stayed bridges by limit cable force measurements. First, a vehicle-bridge model for cable-stayed bridges is presented. Then the relationship between the cable force and moving loads is constructed based on the influence lines. With the hypothesis of sparsity distribution of vehicles on bridge deck (which is practical for long-span bridges), the moving loads are identified by minimizing the ‘l2-norm of the difference between the observed and simulated cable forces caused by moving vehicles penalized by the ‘l1-norm’ of the moving load vector. The resultant minimization problem is convex and can be solved efficiently. A numerical example of a real cable-stayed bridge is carried out to verify the proposed method. The robustness and accuracy of the identification approach with limit cable force measurement for multi-vehicle spatial localization are validated.