| Summary: | Reusing RAP in the base layer became a common practice in the last decade. However, some crucial issues must be resolved to succeed in using RAP satisfying the standard specifications as a base layer. The most important unknown factor is the mechanistic behavior of RAP. This question may be satisfied by understanding the role of RAP in terms of whether it just behaves as a black rock or has a stabilizing effect with traditional aggregates used for base layer.
The first stage of this study is modeling the structural behavior of RAP via prediction MR. This stage then comprises comparing the predicted results to actual measured data under several field conditions. The second stage focuses on the modeling behavior of PD. This stage takes in consideration two sets of data, the first is for the measured PD data calculated from MR test. While another traditional set of measured data for PD from repeated tri-axial loading (RTL) test either single or multi-stage is collected for the same RAP sources used in the first stage. The third stage concerns on MR-PD relationship. It indicates the typical relationship for the MR-PD behavior that can be understood for the RAP in base layer. The fourth and last stage is essential to investigate the Poisson’s ratio of RAP blends and its effectiveness on both parameters MR and PD. This ratio is measured during un-confined compression test. Two main testing conditions: various water and RAP contents are taken in consideration during this measurement for different RAP/Aggregate sources.
This study proves that both prediction models used in the MEPDG for prediction of both parameters MR and PD are totally significant for RAP/Aggregate blends used for pavement base layer. The prediction is at the highest accuracy at water content levels close to OMC%, MDD and with 50% to 75% RAP content. In addition, it is proved that Poisson’s ratio is an effective parameter on both MR and PD parameters especially with variation of water content. This conclusion recommends to take in consideration Poisson’s ratio as an effective parameter in MR and PD prediction models used in MEPDG software. === National Science Foundation (NSF)
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