Stability Analysis of Middle Rock Pillar and Cross - Section Optimization for Ultra - Small Spacing Tunnels
Bibliographic record
Abstract
The ultra-small spacing tunnel is a new tunnel type under the urban engineering environment, which not only has high requirements for deformation and settlement control, but also has great difficulty in construction technology. Since the middle rock pillar is the key part in the design and construction of ultra-small spacing tunnels, to explore the stability of the middle rock pillar in the ultra-small spacing (0-200 mm) subway tunnels, this study examined the Gangding-Shipaiqiao ultra-small spacing tunnels on Guangzhou Metro Line 3 in China by using ABAQUS and theoretical analysis, which mainly involved the plastic zone of the surrounding rock, the stress distribution and skew displacement effect of the middle rock pillar by defining the skew coefficient. The tunnel cross-sections of three different types were optimized, and a simplified calculation method was proposed with the load and ultimate bearing capacity of the middle rock pillar. Results show that the theoretical analysis of the bearing capacity of the middle rock pillar is essentially consistent with the results of numerical calculation. The conclusions obtained in the study are of important theoretical value to provide on-site engineering construction guidance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".