Finite element analysis for rockfall and debris flow mitigation works
Bibliographic record
Abstract
Landslide risks arising from boulder falls and debris flows are commonly mitigated using rigid and flexible barriers. Debris–barrier interaction is a complicated process, so current design methods rely on the use of the pseudo-static force approach. In addition to physical testing, numerical simulations can be used to provide insight into the impact mechanism. This paper presents the applications of numerical models to simulate rigid and flexible barriers subjected to rockfall and debris-flow impacts, respectively. For rigid barriers, rock-filled gabions, a recycled glass cullet, cellular glass aggregates, and ethylene-vinyl acetate (EVA) foam were assessed for their performance as cushioning materials. From the results, empirical equations were established for predicting the boulder impact forces and penetration into the cushion layer. Amongst the materials considered in this study, rock-filled gabions appear to be the most promising for use in practice. For flexible barriers, finite-element models, calibrated using documented case histories, were developed to simulate the debris–barrier interaction. The models were used to investigate the barriers’ behavior under debris impacts from both force and energy perspectives. From the results, the hydrodynamic pressure coefficient was found to be lower than the current recommended value whilst only a small amount of debris energy was transferred to the barrier.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".