Strategies to Reduce Ground Settlement from Shallow Tunnel Excavation: A Case Study in China
Bibliographic record
Abstract
This paper develops a holistic simulation-based approach to determining reasonable strategies to limit the magnitude of ground settlement in shallow tunnel excavation. Simulation models are built to investigate and analyze the impact of different response strategies on the development tunnel-induced movement. A real tunnel case with a shallow buried depth of 5–8 m in the Wuhan metro system in China is utilized to demonstrate the applicability of the developed approach. Results indicate that (1) the simulation technique can be used to model the complex tunnel-soil-ground interaction in a reliable manner and predict the tunnel-induced ground settlement given some grouting strategies are implemented; (2) the optimal control strategy to reduce the tunnel-induced ground settlement should first satisfy the requirement of safety consideration where the ground settlement should be controlled within an allowable range and should then satisfy the cost effective requirement; and (3) the continuous grouting strategy is more suitable to deal with the excessive settlement in case the thickness of covering soil layers is almost equal or less than the tunnel diameter because it can reduce the ground settlement by almost 50%, compared with the situation where no grouting strategies are implemented. The developed approach takes into account both the knowledge of domain experts and computer science techniques and can be used by practitioners in the industry as a decision tool to provide benefits in the development of better alternatives and optimization.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".