Vehicle Load Identification Based on Machine Vision and Influential Line of Optimal Strain Combinations
This paper presents a vehicle load identification method based on machine vision and influential lines of strain combinations optimized by generic algorithms,which can rapidly identify spatio-temporal information of vehicles on orthotropic steel box girder bridge with ease and achieve accurate vehicle load identification without referencing the exact transverse locations of vehicles.First,traffic flow data are collected by the cameras installed on the lateral surfaces of the bridge,based on the machine vision technology,vehicle speed,number and spreads of axles are identified.Second,the strain responses at lower edges of U ribs are collected,and the already-known axle weights of trains are used to mark the strain influential line of vehicle identification points.Third,considering the indexes of variation coefficient,the generic algorithms are used to determine the influential line of optimal strain combinations that are not sensitive to transverse loading locations.At last,the vehicle loads are identified.Numerical modelling and scale-down model test were carried out,with an existing orthotropic steel box girder bridge as a case,to study the identification effects of the proposed method under multiple loading issues,including different types of vehicles,vehicle weights,transverse location and noise levels,and verify the efficiency and noise resistance of the method.It is shown that the method can identify vehicle speed,number and spreads of axles with great accuracy and stability.Following the principle that the influential line of strain combinations optimized by variation coefficient is not sensitive to vehicle transverse locations,the vehicle loads can be effectively identified without pre-estimating the transverse locations of vehicles.The identification bias of axle weight and total weight in numerical simulation are 5.57%and 4.03%(considering 20%noise),respectively,while 7.16%and 4.90%,respectively in model test,proving that the presented vehicle load identification method has high applicability and accuracy,and is easy for engineering application.
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