Successive boulder impacts on the soil-embedded baffle: an experimental study
Bibliographic record
Abstract
Baffles have been used to resist boulder impacts during debris flow. Soil-embedded baffles offer flexibility to prolong the system’s service life across multiple impacts. However, the behaviour of this design is unclear due to limited investigation. This study aimed to experimentally investigate the load and energy transfer mechanisms within a soil-embedded baffle system through heavily instrumented pendular impact tests. Tactile sensors were attached to the baffle foundation to measure contact pressures at the soil–foundation interface during impacts. New scaling laws specific to the soil-embedded baffle system was tailored-derived to ensure model tests data can be scaled up to the prototype. Two tests were conducted on the model baffles in dense and loose soils, with ten successive impacts applied in each to evaluate the system capacity evolution. Significantly less contact force and baffle deflection were observed in looser soil because of greater kinetic energy absorption during impacts. However, the trade-off was larger permanent rotation post-impact. Dynamic compaction due to successive impacts progressively densified the soil, gradually reduces system flexibility and simultaneously increases the contact force. Ignoring the dynamic effects and soil densification led to an underestimation of the peak contact pressure at the soil–foundation interface by over 400% after 10 successive impacts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".