Experimental study of the evolution of soil arching effect under cyclic loading based on trapdoor test and particle image velocimetry
Bibliographic record
Abstract
Arching effect, which is a common phenomenon in any system involving soil–structure interaction, has been found to be inevitably affected by various factors, including loading conditions. This study investigated the evolution of arching effect induced by cyclic loading by conducting a series of tests using a trapdoor apparatus. The test box was instrumented to control the displacement of the moving gate and to record the variation of vertical stress distribution by using a set of dynamic load cells. Digital images were captured during tests and processed using particle image velocimetry (PIV) to determine the displacement field and hence to examine the variation of geometric features of arch and particle movements. The evolution process of arching effect, from the initial formation to the finial collapse, was identified. Depending on the analysis for the geometry appearance, displacement region, and variation of cyclic stresses, both stable and collapsed arches were observed. By increasing the amplitude of cyclic loading step by step, critical loading amplitude corresponding to the threshold of collapse of the arching effect was determined. Based on the results, the effects of trapdoor displacement, cyclic loading frequency, and filling height on arching effect are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".