The Specific Length of an Underground Tunnel and the Effects of Rock Block Characteristics on the Inflow Rate
Bibliographic record
Abstract
The specific length of a tunnel (STL) and a new analytical model for calculating the block surface area of the rock mass are introduced. First, a method for determining the appropriate length of a tunnel for a numerical simulation is described. The length is then used to examine the correlation between the inflow rate to the tunnel and the block volume, the block surface area, and the fracture intensity (P32) through analytical and numerical modeling. The results indicate that the length of the tunnel should at least be equal to the least common multiple (LCM) of the apparent spacings of the joint sets at the wall of the tunnel to obtain the more reliable and immediate results for the inflow rate to a tunnel that is excavated in a fractured rock mass. A new analytical model was developed to calculate the block surface area and determine the essential joint set parameters, which include the dip, dip direction, and spacing. The determination of the rock block characteristics through numerical modeling requires considering the intact block for calculations. The results indicated that the inflow rate to the tunnel increased with an increase in fracture intensity and a decrease in block volume and surface area. The STL and the analytical model used for calculating the block surface area are validated through numerical simulations with 3DEC software version 7.0.
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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".