Estimation of Remaining Service Life of Flexible Pavements from Surface Deflections

Abstract

Remaining service life (RSL) has been defined as the anticipated number of years that a pavement will be functionally and structurally acceptable with only routine maintenance. The Kansas Department of Transportation (KDOT) has a comprehensive pavement management system, network optimization system (NOS), which uses the RSL concept. In support of NOS, annual condition surveys are conducted on the state highway system. Currently KDOT uses an empirical equation to compute RSL of flexible pavements based on surface condition and deflection from the last sensor of a falling-weight deflectometer (FWD). Due to limited resources and large size, annual network-level structural data collection at the same rate as the project level is impractical. A rolling-wheel deflectometer (RWD), which measures surface deflections at highway speed, is an alternate and fast method of pavement-deflection testing for network-level data collection. Thus, a model that can calculate RSL in terms of FWD first sensor/center deflection (the only deflection measured by RWD) is desired for NOS.In this study, RWD deflection data was collected under an 18-kip axle load at highway speed on non-Interstate highways in northeast Kansas in July 2006. FWD deflection data, collected with a Dynatest 8000 FWD on the KDOT network from 1998 to 2006, were reduced to mile-long data to match the condition survey data collected annually for NOS. Normalized and temperature-corrected FWD and RWD center deflections and corresponding effective structural numbers (SNeff) were compared. A nonlinear regression procedure in Statistical Analysis Software (SAS) and Solver in Microsoft Excel were used to develop the models in this study.Results showed that FWD and RWD center deflections and corresponding SNeff are statistically similar. Temperature-correction factors have significant influence on these variables.FWD data analysis on the study sections showed that average structural condition of pavements of the KDOT non-Interstate network did not change significantly over the last four years. Thus, network-level deflection data can be collected at four-year intervals when there is no major structural improvement.Results also showed that sigmoimal relationship exists between RSL and center deflection. Sigmoidal RSL models have very good fits and can be used to predict RSL based on center deflection from FWD or RWD. Sigmoidal equivalent fatigue crack-models have also shown good fits, but with some scatter that can be attributed to the nature and quality of the data used to develop these models. FWD data analysis on the study sections showed that average structural condition of pavements of the KDOT non-Interstate network did not change significantly over the last four years. Thus, network-level deflection data can be collected at four-year intervals when there is no major structural improvement.Results also showed that sigmoimal relationship exists between RSL and center deflection. Sigmoidal RSL models have very good fits and can be used to predict RSL based on cen...

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“…RSL has been defined as the predicted time that a pavement will behave permissibly in terms of function and structure with routine maintenance (Gedafa, 2008). RSL is useful for rehabilitation programs, funds allocation and predicting long-term requirements.…”

“…Determination of the RSL pavement requires the actual characteristics, a description of unacceptable condition and a mechanism to anticipate deterioration. The information required to determine RSL is depicted by Figure 1 (Gedafa, 2008).…”

“…Zaghloul and Elfino utilized expected traffic and back-calculated layer moduli to predict the RSL (Zaghloul & Elfino, 2000). Gedafa suggested sigmoidal models for estimating RSL based on the central deflection from a rolling wheel reflectometer (RWD) or FWD (Gedafa, 2008). On the other hand, approaches to predicting pavement condition can be normally categorized into various classes (Balla, 2010), e.g.…”

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