Paper Info
Title
Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges
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.
Year
DOI
Venue
2019
10.1155/2019/3409525
JOURNAL OF SENSORS
DocType
Volume
ISSN
Journal
2019
1687-725X
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Xudong Jian100.34
Ye Xia210.70
Jose Antonio Lozano-Galant3101.85
Limin Sun422.66