Bibliographic record
Abstract
The rheological behaviour of cement pastes incorporating superplasticisers is influenced by the ambient temperature and mixing time; which is critical for hot weather concreting. Rheological parameters including yield stress and plastic viscosity of cement pastes with a water/cement ratio of 0·38 and incorporating polycarboxylate-, melamine sulfonate-, and naphthalene sulfonate-based superplasticisers were measured as a function of the superplasticiser dosage and temperature (22–45°C) over 20 to 110 min from the time of mixing, with 30 min between successive measurements. The rheological tests were conducted using an advanced shear-stress/shear-strain controlled rheometer. Test results indicate that the yield stress and plastic viscosity of cement paste vary in a linear fashion with the elapsed time, whereas their variations with the temperature and superplasticiser dosage follow power and inverse power functions, respectively. In this study, curves showing shear stress plotted against shear rate for cement pastes incorporating the various superplasticisers at different ambient temperatures and mixing times were predicted using genetic algorithms. Rheological equations were then derived from these curves using the Bingham model and employed to predict the yield stress and plastic viscosity of cement pastes. A sensitivity study was performed to evaluate the effects of mixing time, ambient temperature, and superplasticiser dosage on the calculated yield stress and plastic viscosity. It was shown that the computed yield stress and plastic viscosity values compared well with corresponding experimental data
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".