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原文传递 Assessment of the Traffic Speed Deflectometer in Louisiana for Pavement Structural Evaluation.

题名：	Assessment of the Traffic Speed Deflectometer in Louisiana for Pavement Structural Evaluation.
作者：	Elseifi, M. A.; Zihan, Z. U.
关键词：	Traffic speed deflectometer, Structural evaluation, Falling weight deflectometer, Pavement management system
摘要：	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. 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 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 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. Based on the results of the analysis, it is concluded that the deflection reported by both FWD and TSD for the same locations are statistically different, which was expected 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 Structural Number (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 functional conditions. A methodology was developed to incorporate TSD measurements in the 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 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.
报告类型：	科技报告