Development Of Procedures For Monitoring And Predicting The Long Term Performance Of Continuously Reinforced Concrete Pavements PDF Download

Perpetual pavements represent a significant investment to an owner who has committed to spending additional dollars at initial construction in order to benefit from potential long-term savings from the enhanced performance of this asset. This makes the monitoring of a perpetual pavement critical to ensure that this asset is optimally preserved and maintained in order to meet the expectations of service for the design life and potentially beyond. This Thesis research involved investigation of methods of completing the long-term monitoring of a perpetual asphalt pavement including the development of a testing protocol using a falling weight deflectometer (FWD) as well as a framework for the monitoring of long term perpetual pavement performance. The project site used for the research consisted of one perpetual pavement section (with rich bottom mix (RBM)) which was constructed and instrumented at the Capitol Paving Plant in Guelph, Ontario. It was constructed by a consortium that included the Ontario Ministry of Transportation (MTO), Ontario Hot Mix Producers Association (OHMPA), the University of Waterloo Centre for Pavement and Transportation Technology (CPATT), the Natural Science and Engineering Research Council of Canada (NSERC), Stantec Inc., and McAsphalt Industries Limited. An initial testing program was required to accurately locate the embedded sensors within the test section. This testing program was completed with an array of FWD testing completed within the test section followed by analysis of the response of the embedded sensors to the testing in order to determine their location. This initial testing was successful in determining the embedded sensor locations and the locations were marked in the field for use in future testing programs. The next step consisted of validation of the performance of the embedded sensors. This involved predicting the expected strains using mechanistic design software (Kenpave) followed by a comparison with the strains recorded with the embedded sensors on the site. A significant discrepancy was found between these results and supplemental testing was completed to attempt to isolate and mitigate the source of the variability. The in-situ resilient modulus values were backcalculated using and the FWD results which were adjusted in order to obtain design deflections similar to the deflections measured using the FWD. The resilient modulus of the asphalt concrete layer was adjusted for temperature and the expected strains recalculated using the mechanistic design software. While the results showed signs of converging, the known sources of variability had been evaluated and the remaining difference between the predicted and calculated strain values were considered to be due to a change in the calibration factor of the gauges. New calibration factors were calculated for the gauges and the new calibration factors applied to the sensors and checked using the FWD in order to validate the new calibration factors. The additional testing showed that the embedded sensors were now within the tolerance expected for the types of monitoring equipment used at the site and the new calibration factors were considered to be suitable. Finally, a framework was developed to provide guidance for the long-term monitoring of perpetual pavements using the knowledge and experience gained during the research.