Comparison of empirical and numerical methods in tunnel stability analysis
Bibliographic record
Abstract
The stability of a tunnel can be evaluated using mathematical solutions, empirical methods, or numerical modelling. Mathematical solutions are precise methods; however the need to conduct mathematical calculations usually decreases the user's desire to use this method. Empirical methods are based on the experience gathered by researchers in various parts of the world whereas numerical modelling utilises computing power and, using various modelling techniques, can be a precise way of solving very complex problems. In this method the environment and the geometry can be set by the user. This method allows the user to conduct sensitivity analysis. In this article, empirical methods and numerical modelling using UDEC software were used to conduct a stability analysis of the access tunnel at the Shahriar dam crest, which was one of the most important tunnels of this project. In addition, numerical modelling was used to predict the stresses and deformations around the perimeter of the tunnel, and select the most suitable ground support system. The results obtained from both methods were compared for selection of the best suited support system. The results indicated that the empirical methods presented similar results to the results of numerical modelling at the first stages of tunnel design in jointed rocks. Therefore, in the absence of sufficient information for numerical analysis, the results of the empirical method can be used for this project.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".