Abstract
Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system.
Highlights
Modern bridges are mainly constructed for traffic purposes
The concepts behind BWIM techniques were initially proposed by Moses [6], who used an instrumented bridge as the weighing scale to estimate vehicle weights
One of the most simple and practical BWIM techniques verified by field tests is the gross vehicle weight (GVW)
Summary
Collecting the information of traffic including vehicle weight, velocity, quantity, type, and spatiotemporal distribution, is crucial for bridge design refinement, safety evaluation, and maintenance strategies [1,2,3] To this end, a number of studies on traffic information identification have been conducted. One of the most simple and practical BWIM techniques verified by field tests is the gross vehicle weight (GVW) This identification method is based on the static influence line/surface theory, which is already applied by Moses in his earliest research [13]. Chen et al [19] proposed an identification approach for the spatiotemporal distribution of traffic loads on bridges using the information from the pavement-based WIM and background subtraction technique This approach relies on high quality video image, which limits its range of application. Both the advantages and the potential engineering applications of the methodology are discussed
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