A bridge weigh-in-motion method of motorway bridges considering random traffic flow based on long-gauge fibre Bragg grating sensors. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Lu; Cheng, Xiaoxiang; Wu, Gang
• Is Part Of: Measurement. Volume 186(2021)
• Publication Date: 2021-12
• Subjects: Vehicle parameter identification -- Random traffic flow -- Influence line -- Long-gauge fibre Bragg grating sensors (LFBG sensors); Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110081
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Proposed method shows good performance on vehicle parameters identification under random traffic flow. The effects of the vehicle number, road surface condition and effect of vehicle acceleration are investigated. Proposed method has been verified by bridge-vehicle coupling system and laboratory experiment. Abstract: Accurate traffic conditions and their development regularity can help in the management of future traffic demand and bridge design. This study proposes a bridge weigh-in-motion method for identifying the vehicle parameters of motorway bridges considering random traffic flow and capitalizes on the advantages of long-gauge fibre Bragg grating sensors. A bridge-vehicle coupling system is numerically simulated, a 1:10 indoor experiment is used to verify the proposed method, and the vehicle parameters (e.g., traffic lane, vehicle spacing, vehicle axle weight, and speed) are identified with high accuracy. The effects of the road surface conditions, vehicle numbers, lateral positions and accelerations on the proposed method are investigated, and only find that the lateral positions are obviously related while and others have almost no influence on the method. In addition, the accuracy in axle number and weight identification is not high when the gauge length is smaller than vehicle wheelbases.