Assessment of joint behaviour of immersed tunnel with prefabricated push-out closure joint under differential settlement
Bibliographic record
Abstract
The deformation behaviour of immersed tunnels with prefabricated push-out closure joint remains unclear. In this study, four model tests were conducted to simulate immersed tunnels subjected to differential settlements. The impact of different settlement crossings on joint behaviours was analyzed. Results indicate that differential settlement directly at the joint position causes significant shear displacement, with decreased perpendicular axial movement. The closure joint shows bidirectional axial movement, while the flexible joint primarily undergoes unidirectional compression. The direction and magnitude of joint rotation depend on the settlement location. Differential settlement at a flexible joint significantly influences the rotation of other joints simultaneously. Premature leakage could occur in adjacent flexible joints when differential settlement occurs at the closure joint. Controlling the axial movement of flexible joint is the most effective approach to manage joint opening and prevent leakage under small differential settlements. A closed-form solution was proposed for estimating the axial movement between closure joint and lining structure. It suggests that the interface shear strength should be greater than 0.25 MPa to limit the maximum axial movement of closure joint within the tolerance. Compared to frictional resistance, more attention should be given to the bonding strength provided by post-construction grouting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".