Analysis of In-Situ Dynamic Ground Support Test Results with Insights Revealed by Time-Dependent Terms of Power and Strain Rate
Bibliographic record
Abstract
Abstract The capacity of ground support subjected to dynamic loading is commonly expressed in terms of load, displacement, and energy absorption. Most current laboratory and in-situ dynamic tests of ground reinforcement elements utilize mass drop under gravity from a certain height to generate energy and displacement in the test specimen, but neglect to consider the power and strain rate (time factor terms) in the analysis of test results. Energy and power are very closely related parameters, where power describes how fast energy leaves or comes into a system. Power is an important parameter for describing the impact supplied to a reinforcement element and the resulting reaction. The maximum power a reinforcement element can survive found to be an effective means of making comparisons between reinforcement elements. In this research three case analyses were carried out. These were previous laboratory dynamic tests conducted at the WASM testing facility, the in-situ prototype dynamic testing experiment conducted at the Mt Charlotte Mine, and in-situ dynamic testing carried out using an advanced in-situ dynamic testing rig in nine mines across Australia and Canada. The calculated energy and other parameters from in-situ dynamic tests allowed the formulation of the input power component and strain rate. Relationships were apparent between input energy, average input power, displacement, mechanism of yielding of dynamic reinforcement elements and average strain rate for the tests. The outcomes of the time factor analysis from the three cases were compared to reveal additional information.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".