Abstract
A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.
Highlights
Bridge structural health monitoring (BSHM) has become a pervasive technique that monitors the static and dynamic bridge responses induced by environmental effects or vehicle loads [1]
The initial concepts behind bridge weigh-in-motion (BWIM) were proposed by Moses [11], who used an instrumented bridge as the weighing scale to estimate vehicle weights in his engineering practice
The logic of the paper is as follows: i) both the theoretical background and the application procedure of camera visual sensing and strain sensing is introduced; ii) the influence line theory oriented towards gross vehicle weight (GVW) recognition is elaborated on with an emphasis on the multiple-vehicle problem, iii) overall framework of the data integration methodology for traffic monitoring is summarized; iv) field tests on a concrete box-girder bridge are conducted to demonstrate the proposed methodology, especially for complicated traffic cases
Summary
Bridge structural health monitoring (BSHM) has become a pervasive technique that monitors the static and dynamic bridge responses induced by environmental effects or vehicle loads [1]. The original Moses algorithm used for BWIM purpose has difficulty separating the contribution of the individual vehicles from the bridge response alone when more than one vehicle in adjacent lanes travels side by side on the bridge span This method is unable to identify extra traffic information including types, size, axle number and velocity of vehicles, without the help of additional traffic sensors such as radar, road tubes and embedded axle detectors [15]. The logic of the paper is as follows: i) both the theoretical background and the application procedure of camera visual sensing and strain sensing is introduced; ii) the influence line theory oriented towards gross vehicle weight (GVW) recognition is elaborated on with an emphasis on the multiple-vehicle problem, iii) overall framework of the data integration methodology for traffic monitoring is summarized; iv) field tests on a concrete box-girder bridge are conducted to demonstrate the proposed methodology, especially for complicated traffic cases. The advantages and the potential engineering applications of the methodology are summed up as a conclusion
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