Application of acoustic emission for monitoring shear behavior of bonded concrete–rock joints under direct shear test
Bibliographic record
Abstract
Studying shear behavior and failure mechanism of concrete–rock interfaces between concrete structures and rock mass is highly important. To this end, laboratory direct shear tests were conducted on several bonded interfaces. The effect of normal stress, displacement rate, and bonding percentage on shear behavior of bonded joints were also evaluated. The results showed that the adhesive bond between concrete and rock has the most important effect on shear mechanism of concrete–rock interfaces. When the normal load is low, the asperities do not contribute in shear process, so the shear strength of the joints is governed by adhesive bond. The only contribution of the asperities is that they show a small peak after bond breaking peak. When the normal load is high, the asperities break simultaneously with adhesive bond and they show their contribution in the whole shear process. In a constant normal load, by decreasing bonding percentage, the brittle failure of the bonded joints is changed into softening failure because of less contribution of the adhesive bond and more contribution of the rough asperities in joint failure. Acoustic emission (AE) monitoring of bonded joints showed that for all different tested samples under various loading conditions, there has been no or very few AE activities before adhesive bond breaking point, indicating that other parameters (roughness, normal load, displacement rate) are impressed by adhesive bond and maximum shear strength is determined by this parameter.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".