Characteristics of controlled low-strength materials incorporating cement kiln dust
Bibliographic record
Abstract
This paper presents a study that focuses on evaluating the feasibility of incorporating cement kiln dust (CKD) in the development of controlled low-strength materials (CLSM). A preliminary study (phase I) was conducted (based on fresh and strength properties) to understand the behaviour of 12 selected CLSM mixtures where CKD and cement content varied from 4% to 45% and from 2% to 4% of total mass, respectively. Subsequently, four best CLSM mixes were selected for a detailed study (phase II), which investigated fresh and hardened properties, addressed durability issues, and made recommendations for suitable mix designs for field applications. The research suggests that CLSM with acceptable properties can be developed using moderate volumes of CKD (up to 15% by mass). A combination of 2% cement and 10% CKD or 15% CKD and no cement can provide a mix that satisfies the requirements of a CLSM. Sustainable development in the cement industry can be partly achieved by producing CKD-based CLSM, as it consumes cogenerated products from the cement manufacturing process.Key words: cement kiln dust, controlled low-strength material, mix design, fresh–mechanical properties, durability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".