Comparing Network-Level Structural Measures Impact on Asphalt Pavement Performance Models

Incorporating structural measures into pavement management systems can enhance the underlying performance models. The traffic speed deflectometer (TSD) offers advantages for network-level testing, but it differs from the traditional falling weight deflectometer (FWD) as a structural measure. It is important to investigate which measure provides the most improvement in pavement performance models. This study compares the impact of four structural measures on pavement performance models of four distress rating dependent variables (cracking, rutting, load distress rating, and critical condition index). Structural measures from the Virginia Department of Transportation pavement management data were analyzed, including ground penetrating radar measure of asphalt thickness, FWD measure of structural number, TSD measure of surface curvature index (SCI), and the site’s pre-treatment rate of deterioration. A multilevel model approach related each structural measure to site-specific performance models of asphalt resurfacing treatments. The TSD measure of SCI demonstrated the greatest impact on the cracking, load distress rating, and critical condition index models with increases in marginal R 2 of 0.05, 0.03, and 0.01, respectively. Pavements with higher SCI values reach a failing critical condition index threshold one year sooner. Incorporation of the TSD measure of SCI leads to improved performance models and will benefit pavement management systems.

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Publication Details

Journal
Transportation Research Record Journal of the Transportation Research Board
Published
2025-10-21
DOI
https://doi.org/10.1177/03611981251352512
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
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article

Comparing Network-Level Structural Measures Impact on Asphalt Pavement Performance Models

Bryan C. Smith, Brian L. Smith
Transportation Research Record Journal of the Transportation Research Board
Infrastructure Maintenance and Monitoring
article

Comparing Network-Level Structural Measures Impact on Asphalt Pavement Performance Models

Bryan C. Smith, Brian L. Smith
article en

Abstract

Incorporating structural measures into pavement management systems can enhance the underlying performance models. The traffic speed deflectometer (TSD) offers advantages for network-level testing, but it differs from the traditional falling weight deflectometer (FWD) as a structural measure. It is important to investigate which measure provides the most improvement in pavement performance models. This study compares the impact of four structural measures on pavement performance models of four distress rating dependent variables (cracking, rutting, load distress rating, and critical condition index). Structural measures from the Virginia Department of Transportation pavement management data were analyzed, including ground penetrating radar measure of asphalt thickness, FWD measure of structural number, TSD measure of surface curvature index (SCI), and the site’s pre-treatment rate of deterioration. A multilevel model approach related each structural measure to site-specific performance models of asphalt resurfacing treatments. The TSD measure of SCI demonstrated the greatest impact on the cracking, load distress rating, and critical condition index models with increases in marginal R 2 of 0.05, 0.03, and 0.01, respectively. Pavements with higher SCI values reach a failing critical condition index threshold one year sooner. Incorporation of the TSD measure of SCI leads to improved performance models and will benefit pavement management systems.

Transportation Research Record Journal of the Transportation Research BoardVol. 2679(12)
West Virginia Department of Transportation (US), University of Virginia (US)
Openalex Percentile: Top 68%
Infrastructure Maintenance and Monitoring
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Comparing Network-Level Structural Measures Impact on Asphalt Pavement Performance Models — Bryan C. Smith, Brian L. Smith · Transportation Research Record Journal of the Transportation Research Board (2025) | TGRS Research Map | TGRS