Unveiling Axial Load Transfer Mechanism in Fully Encapsulated Rock Bolts
Bibliographic record
Abstract
Abstract Determining the axial load transfer mechanism of rock bolts under various conditions is paramount for ensuring efficient reinforcement in rock structures, advancing our understanding of rock support and ability to design robust engineering solutions. This paper presents the results of an experimental study aimed at investigating the factors affecting axial load transfer mechanisms in fully encapsulated rock bolts including embedment length, mechanical characteristics of bonding materials, and host rock conditions. The results show that increasing the embedment length improves the pullout capacity, but only up to a critical length, beyond which the ultimate strength and bond stress distribution remain constant. Amongst the mechanical characteristics, shear modulus of the bonding materials was found the most significant factor influencing axial load transfer and the bond stress distribution. Compressive strength of bonding materials, which is commonly used to assess performance, should not be the only factor considered, as shear properties were found more representative. The expansion characteristics of the bonding agents were also found to be effective in improving pullout performance. The study also provides a detailed explanation of how the mechanical properties of host rocks affect the pullout capacity of bolts. Specifically, it states that greater stiffness result in higher pullout strength.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".