Roof stability of tunnels with varied burial depth in rock strata following a modified HB criterion
Bibliographic record
Abstract
Burial depth is one of the most significant factors determining the stability and possible failure patterns of tunnel roofs in rock strata. A tension cut-off (TC) technique, based on the Hoek-Brown (HB) criterion, is essential to accurately characterize the strength and failure behavior of rock under low confinement or tensile stress. The synergistic effects of burial depth and TC on tunnel roof stability still pose a challenge, necessitating the present work. Thus, incorporating the modified HB criterion with TC, failure mechanisms for tunnel roofs with varied burial depth are firstly developed. Based on the limit analysis, the energy balance equation is built by equating the work rates and internal energy dissipation to derive the stability indicators. By optimization codes, the optimal upper-bound solutions, that is the minimum results of the stability number ( N) and factor of safety (FoS), and the maximums of the required supporting pressure ( p) are captured. The effects of burial depth and strength criterion parameters with TC on roof stability are investigated. It is found that N for rectangular tunnel roofs initially decreases and then stabilizes, while for circular tunnels, it first increases, then decreases, and finally stabilizes with increasing burial depth. TC has a decisive influence on both the stability and the critical burial depth for tunnel roofs to form a complete collapse arch. Design charts with application examples are plotted for ready reference and application in practical preliminary design scenarios.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".