Recycled Concrete Aggregate: Viable Aggregate Source for Concrete Pavements
Bibliographic record
Abstract
Virgin aggregate is being used faster than it is being made available creating a foreseeable shortage in the future. Despite this trend, the availability of demolished concrete for use as recycled concrete aggregate (RCA) is increasing. Using this waste concrete as RCA conserves virgin aggregate, reduces the impact on landfills, decreases energy consumption and can provide cost savings. However, there are still many unanswered questions on the beneficial use of RCA in concrete pavements. \n \nThis research addresses the many technical and cost-effective concerns regarding the use of RCA in concrete pavements by identifying concrete mixture and proportioning designs suitable for jointed plain concrete pavements; constructing test sections using varying amounts of RCA; monitoring performance through testing, condition surveys and sensor data; modeling RCA pavement performance; and predicting life cycle costs. \n \nThe research was carried out as a partnership between the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo, the Cement Association of Canada, Dufferin Construction, and the Natural Sciences and Engineering Research Council of Canada. \n \nThe literature review provides an overview of sustainability and key performance indicators, the material properties of RCA both as an aggregate and in concrete, concrete mixture and proportioning designs with RCA, performance of existing RCA pavements, and the implementation of RCA highlighting some examples where RCA has been used successfully. \n \nTwelve preliminary mixes were developed using three total cementitious contents amounts of 315 kg/m3, 330 kg/m3, and 345 kg/m3 to determine four suitable mixes with varying coarse RCA contents (0%, 15%, 30% and 50%) to place at the CPATT test track. At 28-days, all of the twelve mixes exceed the 30 MPa design strength. \n \nFour test sections containing 0%, 15%, 30% and 50% coarse RCA were constructed in June 2007. The test sections had identical cross sections consisting of 250 mm portland cement concrete (PCC), 100 mm asphalt-stabilized OGDL and a 450 mm granular base. For each coarse RCA content, one slab was instrumented with six vibrating wire concrete embedment strain gages to measure long-term longitudinal and transverse strain due to environmental changes, two vibrating wire vertical extensometers to monitor slab curling and warping, two vibrating wire inter-panel extensometers to monitor joint movement, and two maturity meters to measure maturity and temperature. \n \nQuality assurance and quality control (QA/QC) testing showed that the mixes containing RCA exhibited similar or improved performance when compared to the conventional concrete for compressive and flexural strength, freeze-thaw durability and coefficient of thermal expansion. \n \nPavement performance of the four test sections was evaluated using visual surveys following the Ontario Ministry of Transportation’s Manual for Condition rating of Rigid Pavements. Nine pavement evaluations have been performed every two to four months since construction. All test sections are in excellent condition with pavement condition index (PCI) values greater than 85 after two years in-service and approximately three hundred thousand Equivalent Single Axle Loads. \n \nSensor data from the strain gauges, and vertical and inter-panel extensometers are providing consistent results between the test sections. \n \nLong-term performance modeling using the Mechanistic-Empirical Pavement Design Guide (ME-PDG) showed improved performance with respect to cracked slabs, joint faulting, and pavement roughness as the RCA content increased. Multivariable sensitivity analysis showed that the performance results were sensitive to CTE, unit weight, joint spacing, edge support, surface absorption, and dowel bar diameter. \n \nLife cycle cost analysis (LCCA) illustrated the savings that can be expected using RCA as a replacement aggregate source as the cost of virgin aggregate increase as the sources becomes depleted. Multivariable sensitivity analysis showed that the LCCA results were sensitive to construction costs, discount rate, and maintenance and rehabilitation quantities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".