Study On The BWIM Algorithm Of A Vehicle Or Multi-vehicles Based On The Moses Algorithm

Vehicle load is the main exterior load that bridge structure bears for a long time,and it is the main service object of modern bridge too.The damage of bridge structure may come from environment,material aging and earthquake,in addition to appeal reasons,it mainly comes from variable loads such as vehicles.With the advancement of urbanization and the rapid development of economy in China,the development of transportation industry has led to the occurrence of vehicle overweight frequently,which will greatly reduce the service life of bridges and also a great potential safety hazard for drivers and the people.Researchers have developed a bridge weigh-in-motion（BWIM）system for this phenomenon.The bridge weigh-in-motion system can monitor the vehicle passing the bridge,identify the axle load and other information to judge whether the vehicle is overloaded or not,which is a good means to monitor the health status of the bridge.The bridge weigh-in-motion system is a method of identifying vehicle axle load and other information by considering the bridge as a pole scale to weigh vehicle axle load,and by using the dynamic response of bridge structure and other information.Up to now,the research on bridge dynamic weighing algorithm at home and abroad is mainly focused on one-dimensional BWIM system,and most of them only consider the situation of single vehicle crossing bridge,which is inconsistent with the actual situation of multi-vehicle crossing bridge.Therefore,the research of two-dimensional BWIM algorithm has become an urgent problem to be solved.1.For the axle load identification algorithm,the classical Moses algorithm and the derivation process of MFI algorithm are analyzed,the advantages and disadvantages of the two algorithms are compared,and the Moses algorithm which is more suitable for engineering practice is chosen as the basic algorithm in this paper.The influence line is calibrated by Moses algorithm,and the improved Moses algorithm such as Longbo^[39]is applied when the axle load is near.For the axle recognition system,FAD system and wavelet transform method are introduced.Through them,the lateral position of the vehicle can be identified under the condition of the lane where the vehicle is located,and the axle load can be identified under the condition of the axle information.2.The method of single vehicle calibration is used to calibrate the longitudinal impact line at the specific lateral position of the bridge,and the linear interpolation method is used to fit the longitudinal impact line to form a two-dimensional spatial impact surface.Based on the lateral bridge dynamic response line,the measured bending moment value of the vehicle to be identified is split by the calibrated bending moment impact surface,and the multi-vehicle identification problem is reduced to the single vehicle identification problem.Determine the position and number of vehicles in the transverse direction of the bridge,constitute a two-dimensional BWIM algorithm and study its algorithm and working principle.The algorithm can be applied to identify the vehicle information of any vehicle crossing the bridge on all types of bridges that can calibrate the influence surface and the transverse dynamic response line with Moses.In terms of working principle,the workflow of the two-dimensional BWIM system formed by the algorithm is introduced in detail.Relevant requirements for sensor arrangement are put forward.3.Establishing the vehicle-bridge coupling model required for finite element simulation analysis.The finite element method is used to identify the vehicle and the multi-vehicle situation respectively.For single vehicle,the recognition accuracy of the algorithm is discussed when the vehicle in different transverse positions of the bridge;for multi-vehicle,the relationship between the two vehicle masses and the influence of the two vehicles on the recognition accuracy when the vehicle is in different transverse positions of the bridge are discussed,and the recognition of the vehicle running in the same direction and the vehicle running in the opposite direction is also discussed.Finally,this paper identifies the case of three vehicles running in the same direction,and identifies the three vehicles not running in the same direction.The identification accuracy is analyzed with the same quality.The recognition results show that the recognition results of single vehicle and multi-vehicle are of high accuracy,and the recognition of axle load and transverse bridge position of vehicle is good.