Study of dynamic debris impact load on flexible debris-resisting barriers and the dynamic pressure coefficient
Bibliographic record
Abstract
The use of steel flexible barriers to mitigate landslide risk on natural hillsides is becoming common in the past decade in Hong Kong. The current design approach for this kind of barrier structure involves the adoption of the hydrodynamic load model to predict dynamic impact forces, followed by non-linear structural analyses of flexible barriers using numerical programs based on the pseudo-static force method. From local design guidelines, the dynamic pressure coefficient, a critical input parameter in the hydrodynamic load model, is taken as 2.0. This empirically considers the effect of impacts from boulders up to 2.0 m in diameter. With a view to rationalising the design approach, a series of physical impact tests and numerical analyses was conducted to investigate the dynamic debris impact on flexible barriers and the resulting barrier response. The physical impact tests involved up to 9 m3 of debris resisted by a 1.5 m high steel ring-net barrier. The tests were conducted in the 28 m long large-scale flume facility at the Kadoorie Centre in Hong Kong. Numerical modelling using computer programs LS-DYNA and NIDA-MNN was conducted to analyse the dynamic response of flexible barriers with different structural forms. The study aims to evaluate the dynamic pressure coefficient and to verify the current design approach based on the suggested dynamic pressure coefficient from this study. Results indicate that a dynamic pressure coefficient of 1.0 is, in general, appropriate for design purposes if the debris comprises primarily water and fine-grained particles.
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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 | 0.000 |
| 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".