Technical Papers
Mar 14, 2022

Implementing Traffic Speed Deflection Measurements for Network Level Pavement Management in Virginia

Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 148, Issue 2

Abstract

This paper investigates the possibility of using pavement deflection measurements obtained from a traffic speed deflectometer (TSD) for network-level structural evaluation on bituminous pavements in Virginia. Virginia currently uses falling weight deflectometer (FWD) measurements for network-level pavement management decision making for their interstate roads. Two factors were deemed important to determine if FWD data can be replaced with TSD data: (1) the distribution of effective structural number (SNeff) calculated from TSD measurements compared with SNeff calculated from FWD measurements; and (2) the consistency of TSD with FWD in identifying the same weak sections. The results show that the distribution of the SNeff from the measurements from the two devices was similar and the calculated consistency in identifying weak sections between the TSD SNeff and the FWD SNeff was higher than the consistency between the SNeff from two repeated sets of FWD measurements. This suggests that the structural condition obtained from the TSD can be used to replace the structural condition obtained from the FWD currently used in the Virginia Department of Transportation Pavement Management System. The study also assessed whether the choice of the structural index calculated with TSD measurements could potentially have a significant impact on the network-level decision-making process. Very little practical difference was observed between using the SNeff or SCI300 to identify structurally weak sections from TSD measurements. Similar analysis can be performed by other states to incorporate TSD structural conditions into their pavement management decision-making process.

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Data Availability Statement

Some or all data, models, or codes used during the study were provided by a third party. Direct request for these materials may be made to the provider as indicated in the Acknowledgments.

Acknowledgments

We are grateful to ARRB Group Inc. for collecting the TSD data, Infrasense for collecting the Ground Penetrating Radar (GPR) data, and VDOT for help in obtaining the pavement condition data. The results reported in this paper are a part of a broader study conducted for VDOT.

References

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Information & Authors

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Go to Journal of Transportation Engineering, Part B: Pavements
Journal of Transportation Engineering, Part B: Pavements
Volume 148Issue 2June 2022

History

Received: Jun 15, 2021
Accepted: Jan 21, 2022
Published online: Mar 14, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 14, 2022

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Authors

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Graduate Research Assistant, Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute, Blacksburg, VA 24060 (corresponding author). ORCID: https://orcid.org/0000-0003-0494-8926. Email: [email protected]
Samer W. Katicha [email protected]
Research Scientist, Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute, Blacksburg, VA 24060. Email: [email protected]
Gerardo W. Flintsch, M.ASCE [email protected]
Director, Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute, Blacksburg, VA 24060. Email: [email protected]
Brian K. Diefenderfer [email protected]
Principal Research Scientist, Virginia Transportation Research Council, 530 Edgemont Rd., Charlottesville, VA 22903. Email: [email protected]

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Cited by

  • A novel characterisation method for asphalt pavement structural performance assessment based on vehicle vibration data fusion, International Journal of Pavement Engineering, 10.1080/10298436.2024.2308178, 25, 1, (2024).
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  • A Consistency Evaluation Method of Pavement Performance Based on K-Means Clustering and Cumulative Distribution, Applied Sciences, 10.3390/app13010106, 13, 1, (106), (2022).

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