Impact of a damage-based cohesion degradation–friction angle reinforcement model on tunnel surrounding rock stability
Bibliographic record
Abstract
This study addresses the stability of surrounding rock in tunnels constructed through carbonaceous phyllite. A novel damage constitutive model, termed the DP-CWFS model, is proposed by integrating the Drucker–Prager strength criterion with the cohesion weakening–friction strengthening (CWFS) model. The model is implemented in the FLAC3D numerical simulation software via a dynamically linked library (.dll) compiled using Visual Studio 2015. The constitutive model is validated against laboratory triaxial compression test results. The findings demonstrate that the proposed DP-CWFS model effectively captures the elastic behavior, post-yield strengthening, strain-softening characteristics, brittle failure behavior, and residual strength of carbonaceous phyllite. Based on this model, numerical simulations were conducted to analyze stress redistribution, plastic zone evolution, and deformation patterns following excavation of the Yaowangmiao Tunnel in Shaanxi Province, China. The simulation results reveal that when excavation-induced damage is considered, the stress release in surrounding rock is significantly intensified, the plastic zone expands markedly, and the predicted deformations are in good agreement with field monitoring data. The case study confirms that the DP-CWFS model can reliably describe the mechanical evolution of soft rock tunnel surrounding rock under complex geological conditions, offering theoretical and technical support for tunnel stability assessment and support design optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".