TAN Chengjun1,2,ZHAO Hua1,2,ZHANG Bin1,GUO Hongjie1
(1. College of Civil Engineering,Hunan University,Changsha 410082,China; 2. Key Laboratory for Wind and Bridge Engineering of Hunan Province(Hunan University),Changsha 410082,China) 在知网中查找 在百度中查找 在本站中查找
To identify the axle/total weight in the case of multiple vehicles,this paper proposes a twodimensional(2D)-BWIM algorithm considering the lateral position of passing vehicles. In this algorithm,only the responses of girders underneath the traveling lane are adopted to calculate the axle/total weight via using the concept of influence line. Considering the bridge′s 2D behavior,the axle weight is distributed on each girder based on the transverse distribution factors. The influence line of each girder was obtained through a calibration test with known vehicle information. In this study,an iterative method is used to identify the axle/total weight of passing vehicles in multiple vehicles present. This iterative method gives an assumption that the response of each girder is proportional to the transverse distribution factor when a single vehicle crosses the bridge. Using the assumption,the correspond? ing error is calculated based on the calibration test,and the results show that the absolute error is very small and will not affect the accuracy of axle/total weight identification later. Then,three field tests under random traffic were car? ried out to validate the proposed 2D-BWIM algorithm. For a single-vehicle passing over the bridge,the results show that the 2D-BWIM algorithm can significantly improve the accuracy of vehicle axle/total weight recognition compar? ing to the traditional Moses′ algorithm. In this case,for the 2D-BWIM algorithm,the average and variance of errors in total weight identification are 3.1% and 4.8%,respectively. While for traditional Moses′ algorithm,errors of those are 7.9% and 13.5%,respectively. For multiple vehicles presents,the average and variance of errors in axle weight identification of(Moses,2D-BWIM)algorithms are(7.34%,1.53%)and(26.33%,3.12%),respectively.