Prediction of In-Service Pavement Structural Capacity Based on Traffic-Speed Deflection Measurements

Abstract

Nonstructural factors, such as surface distresses and ride quality, have been commonly used as the main indicators of in-service pavement conditions. In the last decade, the concept of implementing a structural condition index in pavement management system (PMS) to complement functional condition indices has become an important goal for many highway agencies. The rolling wheel deflectometer (RWD) provides the ability to measure pavement deflection while operating at the posted speed limits causing no user delays. The objective of this study was to develop a model to predict pavement structural capacity at a length interval of 0.16 km (0.1 mi) based on RWD measurements and to assess its effectiveness in identifying structurally deficient pavement sections. Rolling wheel deflectometer data collected from 153 road sections (more than 1,600 km) in District 05 of Louisiana were used in this study. The predicted structural number (${\mathrm{SN}}_{\mathrm{RWD}0.16}$) showed an acceptable accuracy with a root-mean square error (RMSE) of 0.8 and coefficient of determination ($R^2$) of 0.80 in the validation stage. Core samples showed that sections that were predicted to be structurally deficient suffered from asphalt stripping and material deterioration distresses. Results support that the developed model is a valuable tool that could be used in PMS at the network level to predict pavement structural condition with an acceptable level of accuracy.