Understanding the impact of adhesion on the mechanical behavior of shotcrete
Bibliographic record
Abstract
Shotcrete is widely used as part of ground support in underground excavations. Although adhesion is recognized as a critical mechanical property of shotcrete, there is no strong consensus from a design perspective on whether high or low adhesion is preferred. This is probably due to our relatively limited understanding of the role of adhesion on the field performance of shotcrete. Laboratory investigations can provide useful insights, but it is difficult to extrapolate to field conditions. Numerical models can complement laboratory investigations, but to develop confidence in their results, they must adequately reproduce the failure mechanism of shotcrete. This paper investigates how different adhesion properties control the mechanical behavior of shotcrete. Laboratory tests were used to calibrate a three-dimensional discrete element method model to capture the adhesion failure of a shotcrete liner. The developed model explicitly reproduced the adhesion failure mechanism, including the load redistribution, loading capacity, and displacement of the shotcrete liner. This was used as a basis for a parametric investigation of the impact of adhesion strength on the mechanical behavior of shotcrete. The numerical results indicated the significant implications in selecting adhesion properties of shotcrete and led to recommendations for adhesion capacity under different ground stress conditions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".