Comparative analysis of anchor cables in pullout tests using distributed fiber optic sensors
Bibliographic record
Abstract
The interaction mechanism between grouted anchor cables and the surrounding rock mass or soil are complicated and difficult to investigate using conventional monitoring technologies. This study focuses on the in-situ pullout behavior of grouted anchor cables using Brillouin Optical Time-Domain Analysis technique. A series of pullout tests were conducted on anchor cables with five different anchor lengths and three grouting methods. Distributed fiber optic sensors were used to measure the strain distribution of cable bolts from tip to top. The in-situ pullout test results show that both ultimate pullout resistance and ultimate displacement at the ultimate pullout resistance increase with anchor length. The bond strength suffers a slight variation with an increase in anchor length. Both ultimate pullout resistance and the ultimate displacement for Type-B grouting (grouting with reaming) and Type-C grouting (secondary grouting) are larger than those for Type-A grouting (one-time grouting). The shear bond strength of Type-C grouting is greater than that of Type-A grouting. Results from Gaussian functions analysis show that it is feasible to use the function to characterize the axial stress of anchor cables. Furthermore, the overall mobilized percentage η was evaluated. The anchor cables with smaller anchor lengths will result in greater η. The η for the anchor cables of 6 m reaches 92% at the last load, whereas the anchor cables of 18 m were mobilized by only 29% at the last load. The grouting method has a negligible effect on the η, increasing from 22% to 29% in process of the pullout test.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".