The evolution of grouting pressure and ground deformation induced by synchronous grouting during shield tunneling in soft soil: an investigation based on scaled model test and CEL simulation
Bibliographic record
Abstract
Synchronous grouting is critical for controlling ground deformation during the shield tunneling process in soft soil. The grout filling and spreading mechanism inside the tail-void remains unclear, primarily due to the challenges of observing grout behavior behind the lining in practice and replicating its movement in numerical simulations. This study conducted a scaled model test with a cover–diameter ratio of 1.0, considering varying grouting volume ratios (GVR). A finite element model using the coupled Eulerian–Lagrangian method was established to simulate the grout filling and spreading process. The evolution of ground surface settlement, grouting pressure (GP), vertical displacement, and soil stress around the tunnel were investigated. Results indicated that in soft soil, the grout–mortar initially fills the tail-void before compression. The GPs at the tunnel shoulder and invert were higher than at the crown and waist. Increased GVR caused soil upheaval at the tunnel crown and shoulders and settlement at the invert and spring-line. However, the vertical displacement at the tunnel waist remained unaffected by the variation in GVR. Slight soil stress at the tunnel waist was observed, suggesting a dominant grouting mode of void-filling at the waist.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".