Development of profiler based rideability specifications for flexible pavements

The quality of pavement surfaces is very important for the performance of a pavement facility. Pavement smoothness is one of the most commonly used indices to measure pavement serviceability. Pavement rideability specifications are essential to ride quality control.; The goal of this study is to develop new asphalt concrete pavement rideability specifications for the South Carolina Department of Transportation (SCDOT), as well as investigate the relationship between before- and after-overlay pavement roughness, and the possibility of establishing pavement roughness progression model.; A comparison of surveys indicates that many state highway agencies (SHAs) have migrated to inertial profilers for rideability measurements. High-speed and lightweight inertial profilers were obtained for this study and compared to reference profiles and Mays Ride Meter (MRM) values. The proposed specification is expected to provide an incentive pavement 48% of the time while requiring penalty 12.5% of the time for new pavement. The proposed specification for overlay is based on existing rideability and expected to provide full payment 93% of the time. The proposed specification is based on agency experience; further study is required to determine optimum rideability level from an economic standpoint.; Pavement overlay is a common pavement rehabilitation technique. By applying an artificial neural network (ANN) technique to five years of quality control data from the SCDOT, an excellent prediction of after-overlay rideability is achieved. The analysis also indicates that some pre-overlay treatments may not be beneficial when applied solely for the purpose of improving after-overlay ride. Pre-overlay cold milling of the surface was not found to significantly improve the after-overlay rideability. Additionally, greater overlay thickness was not found to consistently improve after-overlay rideability.; Better prediction of pavement performance is important for specifying achievable rideability level. By reducing the error of the pavement deterioration prediction, agencies can obtain significant budget savings through timely intervention and accurate planning. Best prediction results using ANN based on the Long-Term Pavement Performance (LTPP) program data were obtained on pavements with non-plastic subgrade soil. However, a number of limitations have been identified and should be further studied.