Analysis of rock–support interaction using numerical and multiple regression modeling
Bibliographic record
Abstract
This paper presents the results of performance analysis on the support systems recommended by the RMR (rock mass rating) rock mass classification system. Rock–support interaction is analyzed by means of both numerical and multiple regression modeling. Five different rock mass conditions were assumed from very poor to very good, each representing varied RMR. Extensive computer simulations were conducted to investigate the stresses, displacements, and yielded zones around a circular opening excavated at different depths, and under different rock conditions. The performances of the RMR recommended support systems were analyzed and the stability of excavation was evaluated. Multiple regression modeling was conducted to assess the relationship between support pressure, depth, and tunnel deformation for different rock conditions. Regression models were derived and the response surfaces were constructed, showing the interaction between tunnel depth, support pressure, and tunnel displacement. Using the derived models and the constructed response surfaces, engineers are able to describe the support performance and assess the practical range of expected deformation for their specific site conditions. Also, the approach presented can be used for any special case.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".