A Methodology for the Analysis of Tunnel Intersections Using Two-dimensional Numerical Modeling
Bibliographic record
Abstract
Abstract Intersections of tunnels are an integral component of most underground applications. The design and construction of tunnel intersections can be the most technically challenging element of a tunnel project. Yet, there are relatively few publications and methods regarding tunnel intersection design. Tunnel intersections are essentially a three-dimensional problem. However, 3D models require significantly longer time for solving and interpreting, greater computer resources, and higher user expertise. Hence, there is a great incentive to develop simpler and quicker analysis methods for tunnel intersections. In this paper, a methodology is developed so that a 3D tunnel intersection problem can be modelled using an equivalent 2D numerical model. For this purpose, it is proposed that the main tunnel cross section is modelled with an additional horizontal expansion component that represents the cross tunnel. Multiple 3D and 2D models were computed for the purpose of finding the expansion distances in the 2D models that yield similar deformations to the 3D models. Based on the results, regression analysis was employed to derive a formula relating the ratio of equivalent diameters of the main to cross tunnel to the proper expansion distance. This formula provides a useful and practical tool for preliminary tunnel intersection design. The current work relies on a number of assumptions, mainly that the rock mass is elastic. More work can be carried out to extend and modify the proposed method for more complex conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".