Abstract
Accurate remaining service life (RSL) prediction facilitates effective pavement maintenance strategies, extends service quality, and reduces costs. This study developed RSL prediction models for major distresses in INDOT pavement—including full-depth asphalt flexible, rigid, and composite pavement—using Falling Weight Deflectometer (FWD) and International Roughness Index (IRI) data. The structural and functional prediction models were developed based on the analysis of field data and finite element simulation results. All indicators of the structural prediction models could easily be obtained by processing raw FWD data. The IRI prediction models were developed for INDOT pavements using an enhanced approach for analyzing historical IRI databases. Consequently, the frameworks of maintenance strategy determination were developed using the RSL prediction models and the pavement condition estimation models were developed based on the FWD and IRI data for pavement assessment.
Keywords
remaining service life, pavement structural condition, pavement functional condition, maintenance strategy determination, Falling Weight Deflectometer (FWD), finite element method
Report Number
FHWA/IN/JTRP-2025/10
SPR Number
4443
Sponsoring Organization
Indiana Department of Transportation
Performing Organization
Joint Transportation Research Program
Publisher Place
West Lafayette, Indiana
Date of Version
2025
DOI
10.5703/1288284317854
Recommended Citation
Cho, S., Park, B., Zhang, C., & Haddock, J. E. (2025). Remaining service life prediction of Indiana pavements using mechanistic methods (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2025/10). West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284317854
SPR-4443 Technical Summary