Displacement-based design of shield tunnel lining considering mobilized strength of undrained clay
Bibliographic record
Abstract
This paper presents a novel displacement–performance design method for the shield tunnel based on the mobilized strength design (MSD) theory. First, the nonlinear relationship between the ground volume loss caused by tunnel excavation and the earth pressure was constructed to obtain the real earth pressure on the tunnel lining. Second, the nonlinear relationship between the convergence displacement of the lining and the ground resistance was established to acquire the subgrade reaction acting on the tunnel lining. Finally, the proposed method was verified by orthogonal numerical experiments and compared with the finite element method (FEM) results. Compared with the FEM, the average deviation of the bending moment and the axial force is about 9% and 1%. The average variation of the bending moment can be reduced to 1% after calibration. The results calculated by this developed method are better than the traditional beam–spring model. Its accuracy is close to the FEM based on the strata–structure concept. Therefore, the proposed method based on MSD theory in this paper has the characteristics of high efficiency and simplicity, which provides a new auxiliary design approach for the structural design of shield tunnels in clay areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".