Jet grouting: mathematical model to predict soilcrete column diameter - part I
Bibliographic record
Abstract
Predicting soilcrete column diameter in jet grouting operations is one of the challenging tasks of engineers. By being able to determine the diameters, number of required columns and their spacing needed for activities such as cut off walls, soil improvement and slope stability activities can be calculated. However, there is no precise method to calculate and design the diameter before the operations. Therefore authors attempt to present a mathematical model using sum of squared–deviations (SSDs) method to estimate the diameter of soilcrete created by triple fluid jet grouting system. To do so, the actual data where soilcrete diameters have been measured, is used to prove and test the mathematical model. The effective jet grouting operational parameters that impact the diameter have been determined according to literature review of previous studies. On the basis of the results of modelling, the presented equation can predict soilcrete diameters with error range of 3–20%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".