Application of Constant Resistance and Large Deformation Anchor Cable in Soft Rock Highway Tunnel
Bibliographic record
Abstract
Muzhailing Highway Extra‐long Tunnel in Lanzhou, Gansu Province, China, belongs to the soft rock tunnel in the extremely high geostress area. During the construction process, large deformation of the soft rock occurred frequently. Taking the no. 2 inclined shaft of Muzhailing tunnel as the research object, an NPR (negative Poisson’s ratio) constant resistance and large deformation anchor cable support system based on high prestress force, constant resistance, and releasing surrounding rock pressure was proposed. The characteristics of the surrounding rock under the steel arch support and NPR anchor cable support were compared and analyzed by using 3DEC software. A series of field tests were conducted in the no. 2 inclined shaft, and the rock strength, displacement of the surrounding rock, deep displacement of the surrounding rock, internal force of steel arch, and axial force of anchor cable were measured to study the application effect of the NPR anchor cable support system in tunnel engineering. Moreover, the 3DEC numerical simulation results were compared with the field test results. The research results show that the application of NPR constant resistance and large deformation anchor cable support system in tunnel engineering has achieved good results, and it plays a significant role in controlling the large deformation of the tunnel surrounding rock.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".