Improved shield tunnel design methodology incorporating design robustness
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an improved design methodology for shield tunnels. Here, a new framework for three-dimensional analysis of shield tunnel “performance” (defined herein as the structural safety and serviceability of each tunnel ring) is developed, which considers the effect of the longitudinal variation of input parameters on the tunnel performance. Within this framework, random fields are used to simulate the longitudinal variation of input parameters, and the three-dimensional problem of shield tunnel performance is solved through a two-stage solution involving a one-dimensional model (for tunnel longitudinal behavior) and a two-dimensional model (for performance of segment rings). Furthermore, the robust design concept is integrated into the design of shield tunnels to guard against the longitudinal variation of tunnel performance caused by the longitudinal variation of input parameters. In the context of robust design, a new measure is developed for determining the robustness of the tunnel performance against the longitudinal variation of noise factors. A multi-objective optimization is then performed to optimize the design with respect to the design robustness and the cost efficiency, while satisfying the safety and serviceability requirements. Through an illustrative example, the effectiveness and significance of the improved shield tunnel design methodology is demonstrated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it