Effects of particle size and cushioning thickness on the performance of rock-filled gabions used in protection against boulder impact
Bibliographic record
Abstract
A gabion is one of the most commonly used cushioning layers to shield protection structures against boulders entrained in debris flow. Despite the prevalence of gabions, their cushioning performance is highly variable because of the wide range of rock sizes and cushioning thicknesses that are recommended in the literature. Correspondingly, the dynamic response of gabion cushioning layers varies dramatically. In this study, large-scale pendulum impact tests were used to calibrate a discrete element model. Subsequently, a parametric study was carried out to discern the effects of particle size and cushioning thickness on the impact load and transmitted load exerted by a boulder. Results reveal that as the particle size in the cushioning layer decreases, the force chains collapse more easily, and the expansion angle of strain energy increases. To optimize the performance of a gabion cushioning layer, practitioners should reduce the size of the particles to a normalized particle radius of about 0.1. A normalized particle radius less than 0.2 ensures that the expansion angle of strain energy is large enough — greater than 45° in this study — so as to enable load spreading across the barrier. To eliminate the effects of energy reflecting off the barrier and directed back to the point of impact, which augments the impact load, the cushioning layer thickness should be greater than three times the radius of the boulder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".