Penetrability of microfine cement grouts: experimental investigation and fuzzy regression modeling
Bibliographic record
Abstract
One-dimensional injection tests were conducted on sand columns with a height of 134 cm for the penetrability evaluation of microfine cement grouts. Three ordinary cement types were pulverized to obtain microfine cements having nominal maximum grain sizes of 20 and 10 μm, and these cements were used in the present investigation. Suspensions with water to cement (W/C) ratios of 1, 2, and 3, by weight, were injected into 13 clean sands with d15 ranging from 0.17 to 2.25 mm and Cu ranging from 1.19 to 6.67. Pulverization of the ordinary cements to produce microfine cements extends the range of groutable sands to “medium-to-fine”. Cement fineness; suspension W/C ratio and apparent viscosity; and sand grain size, gradation, and relative density are very important parameters, as they substantially affect both grout penetration and maximum injection pressure. The penetration length of cement grouts was correlated to parameters pertinent to the suspension, sand, and injection process by performing fuzzy and ordinary linear regression analyses of the injection test results. The resultant fuzzy regression models provided successful penetration length predictions for the majority of the cases analyzed, while the best ordinary regression model exhibited a correlation coefficient not higher than 0.363.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".