Investigation of prestress loss in anchor cables and adaptive construction scheme for soft-rock tunnelling
Bibliographic record
Abstract
The construction and stabilisation of soft-rock tunnels pose significant challenges due to rock's inherent properties, like low strength and high deformability. Traditional passive support systems often fail to effectively control tunnel deformation, leading to increased construction costs and delays. Alternatively, the adoption of prestressed anchor cables as an active support has proven effective in controlling soft-rock tunnel deformation. However, the issue of prestress loss in anchor cables has received scarce attention, hindering the widespread application of this active support system. This study investigates prestress loss in anchor cables and evaluates how their composition design and construction affect anchoring performance in the soft rock Muzhailing tunnel in China. Through comprehensive field and laboratory tests, the research identifies that the original design of prestressed anchor cable system led to a significant prestress loss rate of 30%–55%. To mitigate this, an optimised construction scheme was proposed, adjusting specifications, configurations, and construction techniques according to geological conditions. Implementation of this scheme reduced the prestress loss to 25%–35%. Based on these results, a generalised adaptive construction scheme for using prestressed anchor cables to support soft-rock tunnelling is proposed, aiming to provide some practical guidelines for enhancing soft-rock tunnel structural integrity and construction safety.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".