A correction algorithm for automatic pavement detection data bias
ID:2052 View Protection:ATTENDEE Updated Time:2021-12-08 10:21:46 Hits:291 Poster Presentation

Start Time:2021-12-17 09:06(Asia/Shanghai)

Duration:1min

Session:P2 Poster2021 » P2T1Track 1 Advanced Transportation Information and Control Engineering

Presentation File Attachment File

Tips: Only the registered participant can access the file. Please sign in first.

Abstract
The inspection results of the automated pavement inspection equipment often deviate from the pavement's actual condition, which leads to misjudgment of the pavement's technical condition. This study takes the inspection data obtained by the automated equipment commonly used on Shanghai highways as an example and proposes a data correction process that considers the pavement damage composition characteristics. Based on 10.8 km of automated equipment and manual comparative inspection testing, the study suggests a data correction algorithm based on the single damage index and establishes a PCI (Pavement Condition Index) calculation model based on automated inspection data that integrates block crack, alligator crack, line crack, and the interaction term. Finally, the proposed correction model is validated with actual pavement measurement data, and the results show that the model has good accuracy.
Keywords
CICTP
Speaker
Li Li
Shanghai University

Jiahui Yu
Shanghai University

Submission Author
Li Li Shanghai University
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Dec 17

    2021

    to

    Dec 20

    2021

  • Dec 16 2021

    Contribution Submission Deadline

  • Dec 24 2021

    Registration deadline

Sponsored By
Chinese Overseas Transportation Association
Chang'an University
Contact Information