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This project was performed to evaluate the performance of recycled aggregates and large stones used in the aggregate base/subbase layers of pavement systems and provide recommendations regarding pavement design and material selection. As part of this project, eleven test cells were built at MnROAD to evaluate the impact of recycled aggregates and large stones on the long-term pavement performance via a series of laboratory [permeability, soil-water characteristic curve (SWCC), stereophotography (image analysis), gyratory compaction, and resilient modulus (MR) tests] and field tests [intelligent compaction (IC), falling weight deflectometer tests (FWD), rutting measurements, international roughness index (IRI) measurements, light weight deflectometer (LWD) tests, and dynamic cone penetrometer (DCP) tests]. In addition, a pavement mechanistic-empirical (ME) design approach was used to provide recommendations for designs of pavement systems containing recycled aggregate base (RAB) and large stone subbase (LSSB) layers. Overall, this project found that finer recycled concrete aggregate (RCA) material would be preferable to coarser RCA material and a blend of RCA and recycled asphalt pavement (RAP) materials would be preferable to natural aggregate for aggregate base layers. RCA materials provided better performance than the blend of RCA and RAP materials, indicating that RCA materials would be preferable to the blend. For LSSB layers, this project found that geosynthetics would be required to successfully construct thinner LSSB layers. Overall, thicker LSSB layers provided better structural support than thinner LSSB layers both in the short term and the long term.

The use of recycled materials promotes sustainability in roadway construction by reducing the consumption of energy and emission of greenhouse gases associated with mining and the production of virgin aggregate (VA). Recycled asphalt pavement (RAP) and recycled concrete aggregate (RCA) have comparable characteristics to VA that have been used in roadway base course applications. This study develops a database for RAP and RCA material characteristics, including gradation, compaction, resilient modulus (Mᵣ), California bearing ratio (CBR), and saturated hydraulic conductivity (Kₛₐₜ). In addition, this study summarizes construction specifications provided by several departments of transportation (DOTs) regarding the use of recycled aggregates in pavement systems. The effects of the presence of RAP and RCA in aggregate matrices on the engineering and index properties of aggregates are investigated and some trends are observed. For example, the study finds a higher RAP content reveals a higher summary Mr (SMr), and a higher RCA content causes an increase in optimum moisture content (OMC) and a decrease in maximum dry unit weight (MDU). In addition, a series of AASHTOWare Pavement Mechanistic-Empirical (ME) Design (PMED) analyses are conducted for three traffic volumes [low (1,000 AADTT), medium (7,500 AADTT), and high (25,000 AADTT)] with the material inputs collected for the database to determine whether different values of different characteristics of RCA and RAP can be used in flexible/rigid pavement designs. Results show that Mr has a higher effect on pavement distress predictions compared to gradation and saturated hydraulic conductivity (Kₛₐₜ).

Seasonal freeze-thaw weakening has a significant effect on pavement foundation performance. The seasonal freeze-thaw cycles cause extensive damage to the pavement from frost-related problems such as frost heave, frost boils, thaw weakening, total rutting, and degradation of mechanical properties. Changes in temperature of pavement foundation geomaterials during freeze-thaw cycles can significantly influence the performance of pavement foundation layers. It is crucial to monitor the changes in water content, temperature, and matric suction of aggregate base and subgrade soils to be able to predict the frost depth, freezing and thawing times, and number of freeze-thaw cycles. This project has two main goals: (1) develop a data-driven model to predict the maximum/minimum frozen soil depths and (2) freezing and thawing duration and numbers via use of standard climate data that includes precipitation, shortwave radiation, and air temperature. During this research, a model was developed and validated using the climate and environmental data collected from MnDOT. As a result of this research an Excel tool was developed that can predict frost depth, soil temperature, number of freeze-thaw cycles, and duration of freezing and thawing periods at a given soil depth via use of weather data. The required climate data include air temperature, relative humidity, wind speed, precipitation, and solar radiation.

These appendices pertain to report NRRA202103, Determining Pavement Design Criteria for Recycled Aggregate Base and Large Stone Subbase.