A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology
Identification of spatio‐temporal distribution of vehicle loads is very important for understanding the exact loading conditions and behaviors of long‐span bridges. Using computer vision technology combining the monitoring information of the weigh‐in‐motion system (WIM) at one cross‐section and the camera along the bridge, a method to identify the spatio‐temporal distribution of vehicle loads for long‐span bridges is proposed. For moving vehicles, template images were sampled from the camera video at the location of the WIM, and the weight information of the captured vehicle was extracted from the output data sheet of the WIM based on the relationship of the pass time. Template matching and particle filter techniques were used to track the moving vehicle loads on the bridge. The images were processed using the computer vision technology. The video images obtained from cameras and the weight information measured by the WIM on the Hangzhou Bay Bridge were employed in this study. The effectiveness and accuracy of the proposed algorithm were validated through the in situ field test results on the Hangzhou Bay Bridge. Copyright © 2015 John Wiley & Sons, Ltd.
Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology
Identification of spatio‐temporal distribution of vehicle loads is very important for understanding the exact loading conditions and behaviors of long‐span bridges. Using computer vision technology combining the monitoring information of the weigh‐in‐motion system (WIM) at one cross‐section and the camera along the bridge, a method to identify the spatio‐temporal distribution of vehicle loads for long‐span bridges is proposed. For moving vehicles, template images were sampled from the camera video at the location of the WIM, and the weight information of the captured vehicle was extracted from the output data sheet of the WIM based on the relationship of the pass time. Template matching and particle filter techniques were used to track the moving vehicle loads on the bridge. The images were processed using the computer vision technology. The video images obtained from cameras and the weight information measured by the WIM on the Hangzhou Bay Bridge were employed in this study. The effectiveness and accuracy of the proposed algorithm were validated through the in situ field test results on the Hangzhou Bay Bridge. Copyright © 2015 John Wiley & Sons, Ltd.
Identification of spatio‐temporal distribution of vehicle loads on long‐span bridges using computer vision technology
Chen, Zhicheng (author) / Li, Hui (author) / Bao, Yuequan (author) / Li, Na (author) / Jin, Yao (author)
Structural Control and Health Monitoring ; 23 ; 517-534
2016-03-01
18 pages
Article (Journal)
Electronic Resource
English
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