Use of High-Range Water Reducer to Mitigate the Detrimental Effect of Recycled Acrylic-Based Polymers on Concrete Performance
Bibliographic record
Abstract
The incorporation of recycled polymers from waste latex paints (WLPs) in concrete manufacturing is very limited. In fact, this practice cannot be formally implemented without thorough assessment of the various implications that could result from WLP additions on concrete properties. This paper seeks to provide better understanding on effect of WLP constituents and substitution rates on concrete fresh and mechanical properties. It also examines the effect of incorporating naphthalene-based high-range water reducer (HRWR) to mitigate the detrimental effects associated with such additions. Tested WLPs were not randomly collected from waste collection sites; rather produced to assure full traceability of composition and then stored for around 1-year to expire. Test results have shown that workability, setting time, and hardened properties of concrete are directly affected by the polymer latex type and content including the pigment/extender ratio. The use of HRWR is efficient to control work ability and its loss over time when WLPs are disposed in concrete at relatively high rate of 10% of mixing water. The incorporation of HRWR should be coupled with certain reduction in free water to control the drop in compressive strength and bond to embedded steel bars.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".