Parameters affecting the thickness of bentonite cake in cutoff wall construction: case study and physical modeling
Bibliographic record
Abstract
Concrete cutoff walls are usually constructed using a panel-by-panel technique in which primary panels are constructed with space between them and then secondary panels are constructed and inserted in the spaces. A small thickness of residual bentonite cake from the slurry used during excavation usually remains in the construction joints between adjacent primary and secondary panels. The thickness of such bentonite-filled joints should be minimized in terms of the performance of the cutoff wall in controlling seepage. This research experimentally evaluated the effects of a number of design and construction parameters on the thickness of the bentonite cake using data from a case study (the cutoff wall of Karkheh Dam). A physical model test was developed and a number of tests were conducted. The test results showed that parameters such as age of the primary panels, cement content of the slurry, quantity of additives in the slurry, and circulation versus noncirculation of the slurry are responsible for the thickness of the bentonite cake. The results are presented and analyzed.Key words: cutoff wall, plastic concrete, bentonite slurry, bentonite cake, physical model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".