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

作者：Bao, Yuequan; Li, Hui^*; Chen, Zhicheng; Zhang, Fujian; Guo, Anxin

来源：Structural Control & Health Monitoring, 2016, 23(1): 144-155.

DOI：10.1002/stc.1763

• structural health monitoring
• moving heavy vehicle loads identification
• spatial distribution
• sparse l(1) optimization
• cable-stayed bridge

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.

• 出版日期2016-1
• 单位哈尔滨工业大学