Proposed Method for Determining the Residual Mortar Content of Recycled Concrete Aggregates
Bibliographic record
Abstract
Abstract Recycling concrete from demolition of existing structures and using it as recycled concrete aggregates (RCAs) in structural-grade concrete have significant economic and environmental benefits. Currently, only a small portion of the concrete waste is reused in building construction, while most of it is used as either pavement base course or sent to landfills for disposal. The lack of confidence in the material properties of the concrete produced with RCAs is generally the main reason for its under-utilization in structural concrete. It has been demonstrated in the literature that the amount of residual mortar attached to the original (or “virgin”) aggregate particles is one of the factors affecting the material properties of RCAs. Therefore, before using RCAs in new concrete, it is crucial that the residual mortar content (RMC) is determined accurately; however, currently there is no standard procedure to determine this quantity. In this paper, an experimental method is proposed to determine the RMC of RCAs. The method comprises a combination of mechanical and chemical stresses that disintegrate the residual mortar and destroy the bond between the mortar and the natural aggregates. The mechanical stresses are created through subjecting RCA to freeze-and-thaw action, while the chemical degradation is achieved through exposure of the RCA to a sodium sulphate solution. The results of the proposed test procedure are validated by means of comprehensive image analysis. With the proposed approach, the attached residual mortar can be adequately removed, and the residual mortar content can be determined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".