Prediction and analysis of subsidence induced by shield tunnelling in the Madrid Metro extension
Bibliographic record
Abstract
The development of tunnelling projects under heavily populated cities has been rapidly increasing around the world during the last decades. Since tunnel construction can have disastrous effects on buildings, structures, and utilities near the excavation, construction methods have necessarily to provide maximum safety inside and outside the tunnel. To predict and correct dangerous ground movements due to the tunnelling works, the authors developed a numerical model to simulate the earth pressure balance (EPB) excavation procedure and injection to complement some deficiencies found in previous analytical or empirical subsidence estimating procedures. This model takes into account the full excavation sequence and has been validated by a large amount of monitoring data from the previous Madrid Metro extension. In the present paper, several predictive methods are used to predict the ground movements generated during a new Madrid Metro extension project consisting of 48 km of tunnel (19992003). At the end of the works the results will be compared with data from monitored sections placed in all five cities linked by the extension. Conclusions about the applicability and accuracy of the methods will be established with the aim of helping researchers and engineers in their future projects.Key words: ground movements, monitoring, numerical modelling and analysis, settlement, tunnels.
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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 | 0.000 | 0.001 |
| 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 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".