Assessment of the Traffic Speed Deflectometer in Louisiana for Pavement Structural Evaluation
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2018-03-01
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TRIS Online Accession Number:01671005
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Edition:FINAL REPORT
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NTL Classification:NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance
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Abstract:Many state agencies have recognized the importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation (M&R) strategies along with functional indices. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The Traffic Speed Deflectometer (TSD) has emerged as a continuous pavement deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The research objective of this study was to assess the feasibility of using TSD measurements at the network-level for pavement conditions structural evaluation in Louisiana and in backcalculation analysis. To achieve the objectives of the study, TSD and Falling Weight Deflectometer (FWD) measurements were collected in District 05 of Louisiana and data were available from experimental programs conducted at the MnROAD research test facility and in Idaho. TSD measurements were compared with FWD deflection measurements to evaluate the level of agreement and difference between the two devices. Based on this evaluation, a SN predictive model was developed and validated to assess the structural conditions of in-service pavements. The model was then used to identify structurally sound and structurally deficient in-service pavements. Furthermore, a methodology was developed and was validated to backcalculate the layer moduli from TSD measurements. Based on the results of the analysis, it is concluded that the deflection reported by both FWD and TSD for the same locations are statically different, which is reasonable given the differences in loading characteristics and load type between the two devices. It is also concluded that surface roughness has a notable effect on TSD field measured deflections. The present study successfully developed a model to predict in-service SN based on TSD deflections at 0.01-mile interval of a road section. The model was successfully developed and validated with SN calculated based on TSD and FWD deflection data obtained from two contrasting data sets from Louisiana and Idaho. Furthermore, the estimated percentage loss in structural capacity from the model was in good agreement with the percentage loss calculated from FWD. The importance of considering structural indices along with functional indices was demonstrated based on statistical analysis and extracted cores. Core samples showed that the sections that were predicted to be structurally deficient suffered from asphalt stripping and debonding problems. Yet, some of these sections were in very good conditions according to their functional indices. A methodology was developed to incorporate TSD measurements in backcalculation analysis and for predicting pavement layer moduli. The proposed Artificial Neural Network (ANN) model showed acceptable accuracy in predicting the corresponding FWD deflections (TSD*) from TSD deflection measurements with a coefficient of determination of 0.90. In addition, the backcalculated moduli from FWD and TSD* deflection measurements were in good agreement. The Root Mean Square Error (RMSE) was 12.5%, 13.2%, and 10.2% for the AC moduli, base moduli, and subgrade moduli, respectively. The ANN model was also validated by comparing the critical pavement responses, number of cycles for fatigue failure, and Structural Health Index (SHI) calculated from FWD and TSD* measurements.
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