Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach
Bibliographic record
Abstract
Abstract An analytical method and numerical simulation were developed to investigate the shear performance of fiberglass rock bolts (20-tonne and 30-tonne) by conducting sixteen double-shearing tests with both clean and infilled shear interfaces. Following the preparation of the required samples, each test set-up was subjected to different ranges of pretension values. The infilled scenario involved 5 mm thick sandy clay infilled shear interfaces. The results of the double shearing tests unveiled that as pretension increased, so did the confining pressures at the shear interfaces for both clean and infilled joints. Also, an analytical model was developed utilising the Fourier transform, energy balance theory, and linear elastic theory. The result was an empirical relationship that could determine the double shear performance of fibreglass rock bolts in close agreement with the experimental data. Coefficients were incorporated to facilitate model calibration and tuning. Eventually, fast Lagrangian analysis of continua (FLAC) three-dimensional (3D) modelling was utilised to conduct numerical simulations of fibreglass rock bolts subjected to double shearing scenarios. The numerical model was calibrated against experimental data and then extended to conduct a sensitivity analysis on fibreglass rock bolts subjected to double shear test setup variations. Scenarios included rock bolt installation angles, shearing rates, and various host rock strengths. The results revealed that increasing the shear speed from the experimental test baseline yielded substantial displacement increases in the post-failure residual performance of the rock bolts. Changing the installation angle resulted in greater peak shear forces and extended residual zones. The least significant impacts were observed when changing the host rock UCS, suggesting neither rock bolt was drastically impacted by weak or strong host rocks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".