Prediction of grout penetration in fractured rocks by numerical simulation
Bibliographic record
Abstract
As fractures in rock significantly reduce the strength as well as the stiffness of the rock mass, grouting may be required to improve the performance of the rock mass in engineering or mining projects. During grouting, mortar of cement or other materials is injected into the rock mass so that the fractures can be filled up and the rock mass can act as an integral unit. Unlike water, grouts are usually viscous and behave as non-Newtonian fluids. Therefore, the equations describing the flow of grout are more complicated and the solutions are quite difficult to obtain. The problem is further aggravated by the fact that the fractures are mostly randomly distributed, and it is rarely possible to accurately define the fractures and the distribution patterns. In this paper, a numerical model is proposed for analyzing the grouting process. The model is based on the stochastic approach, and it can provide the depth of penetration and the fluid pressure due to the flow of grout, which is modeled as a Bingham fluid, in the fractured rock mass. Parametric studies have been carried out to investigate the effects of various factors on the depth of penetration, and a regression formula is developed for calculating the penetration depth. Experiments have been carried out and their results are used to validate the present method.Key words: stochastic fractures, fractured rock mass, grout flow, grout penetration.
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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 | 0.001 |
| 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".