A comprehensive review of experimental studies on shear behavior of bolted rock joints with varying rock joint and bolt parameters and normal stress
Bibliographic record
Abstract
Abstract The shear characteristics of bolted rock joints are crucial for the stability of tunneling and mining, particularly in deep underground engineering, where rock bolt materials are exposed to high stress, water pressure, and engineering disturbance. However, due to the complex interaction between bolted rock joints and various geological contexts, many challenges and unsolved problems arise. Therefore, more investigation is needed to understand the shear performance of bolted joints in the field of deep underground engineering. This study presents a comprehensive review of research findings on the responses of bolted joints subjected to shearing under different conditions. As is revealed, the average shear strength of bolted rock joints increases linearly with the normal stress and increases with the compressive strength of rock until it reaches a stable value. The joint roughness coefficient ( JRC ) affects the contact area, friction force, shear strength, bending angle, and axial force of bolted rock joints. A mathematical function is proposed to model the relationship between JRC , normal load, and shear strength. The normal stress level also influences the deformation model, load‐carrying capacity, and energy absorption ratio of bolts within bolted rock joints, and can be effectively characterized by a two‐phase exponential equation. Additionally, the angle of the bolts affects the ratio of tensile and shear strength of the bolts, as well as the mechanical behavior of both bolted rock joints and surrounding rock, which favors smaller angles. This comprehensive review of experimental data on the shear behavior of bolted rock joints offers valuable theoretical insights for the development of advanced shear devices and further pertinent investigations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".