Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

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 theo...

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Bibliographic Details
Published in:Journal of sensors 2019-01, Vol.2019 (2019), p.1-15
Main Authors: Sun, Limin, Lozano-Galant, Jose Antonio, Xia, Ye, Jian, Xudong
Format: Article
Language:English
Subjects:
Algorithms
Box girder bridges
Box girders
Bridge loads
Bridge maintenance
Bridges
Cameras
Computer vision
Continuous bridges
Detection
Engineering
Field study
Field tests
Heavy duty trucks
Identification
Influence lines
Load
Machine learning
Methodology
Methods
Motion systems
Neural networks
Sensors
Statistical analysis
Strain gauges
Structural analysis
Structural health monitoring
Surveillance
Temperature effects
Theory
Traffic information
Traffic safety
Vehicles
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Summary: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.
ISSN:1687-725X
1687-7268
DOI:10.1155/2019/3409525