Investigating ground movements caused by the construction of multiple tunnels in soft ground using laboratory model tests
Bibliographic record
Abstract
The prediction of the ground movements above single tunnels in soft ground is well established and can be estimated using semi-empirical methods based on the Gaussian curve. However, the prediction of ground movements associated with closely spaced multiple tunnels, in particular side-by-side (sbs) tunnels, is not as well understood, and therefore simple predictive methods for this application are currently quite limited in terms of their accuracy. This paper describes results from a series of small-scale (1/50) laboratory model tests (conducted at 1g) carried out in Speswhite kaolin clay. These tests have been conducted to gain a greater understanding of the short-term ground movements associated with closely spaced multiple (sbs) tunnels. The observed ground movement results from these tests have shown many of the characteristics observed at full-scale in the published case studies. These results are compared to the commonly used Gaussian curve prediction method and demonstrate the potential inaccuracy in this approach for predicting ground movements associated with closely spaced multiple tunnels. A method that modifies the Gaussian curve approach is also applied to the laboratory data and shows improved predictions.Key words: tunnelling, ground movements, multiple side-by-side tunnels, physical modelling, settlement prediction.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".