Sparse l1 optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges

A method for identifying the distribution of moving heavy vehicle loads is proposed for cable-stayed bridges based on a sparse l(1) optimization technique. This method is inspired by the recently developed compressive sensing (CS) theory, which is a technique for obtaining sparse signal representations for underdetermined linear measurement equations. In this study, sparse l(1) optimization is employed to localize the moving heavy vehicle loads of cable-stayed bridges through cable force measurements. First, a simplified equivalent load of vehicles on cable-stayed bridges is presented. Then, the relationship between the cable forces and the moving heavy vehicle loads is established based on the influence lines. With the hypothesis of a sparse distribution of vehicle loads on the bridge deck (which is practical for long-span bridges), moving heavy vehicle loads are identified by minimizing the `l(2)- norm'of the difference between the observed and simulated cable forces caused by the moving vehicles penalized by the `l(1)- norm' of the moving heavy vehicle load vector. A numerical example of an actual cable-stayed bridge is employed to verify the proposed method. The robustness and accuracy of this identification approach (with measurement noise for multivehicle spatial localization) are validated. Copyright (C) 2015 John Wiley & Sons, Ltd.