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The National Academies

NCHRP 20-50(22) [Completed]

LTPP Data Analysis: Feasibility of Using LTPP Data to Improve Use of FWD and Longitudinal Profile Measurements

  Project Data
Funds: $100,000
Research Agency: Michigan State University
Principal Investigator: Karim Chatti
Effective Date: 9/1/2019
Completion Date: 10/30/2020

BACKGROUND: Pavement deflections obtained from falling weight deflectometer (FWD) measurements and roughness obtained from longitudinal profile measurements are used together with other measurements to assess pavement condition.  These measurements are influenced by the temporal and diurnal changes; the consideration of this influence is necessary for accurate assessment of pavement condition.

The Long-Term Pavement Performance Program (LTPP) Seasonal Monitoring Program (SMP) was initiated to obtain data on the influence of temporal changes on pavement deflection and roughness. However, the temporal and diurnal data contained in the LTPP database have not been applied to improve the practices of measuring deflection and roughness, and their use has not been demonstrated. In addition, there is a concern about the adequacy of available LTPP data to accomplish this task. There was a need to assess the feasibility of using the LTPP SMP and diurnal measurements for developing guidelines for improved use of FWD and longitudinal profile measurements. The findings of this assessment will help make a decision regarding the need for further research.

 

OBJECTIVE: The objective of this research was to evaluate the feasibility of using data from the LTPP SMP and diurnal measurements for developing guidelines for improved use of FWD and longitudinal profile measurements data in evaluating pavement condition. The research shall address both asphalt and concrete pavements.

 

STATUS: Research is complete. The  contractor's final report is available at  

http://onlinepubs.trb.org/Onlinepubs/nchrp/docs/NCHRP20-50-22_FRandAppendices.pdf

In reviewing the final report, a concern has been expressed regarding the evaluation of data quantity and its adequacy. 

 


 

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