Impact of sample preparation on erosion characteristics and subsequent mechanical behaviour of gap-graded soils: an imaging-based analysis
Bibliographic record
Abstract
This study investigated the influence of sample preparation methods, moist tamping and wet pluviation, on the erodibility and mechanical behaviour of gap-graded soils with three gradations: fully stable, unstable, and on the borderline of stability. Drained triaxial tests were performed using a modified erosion-triaxial apparatus, followed by micro-CT scanning to assess pore network properties. The results indicated that for fully stable and fully unstable samples, the preparation method had minimal impact on both erosion and mechanical behaviour. However, for the samples on the borderline of stability, wet pluviation method resulted in fine particle segregation, creating a heterogeneous structure with reduced pore connectivity. This led to lower erosion rates (0.4 gr/min reduction compared to the moist tamping technique), but mechanical properties remained largely unaffected, as confirmed by similar intergranular void ratios and stress–strain responses. Micro-CT scanning quantified differences in pore structure, showing that wet pluviation samples exhibit lower connected porosity compared to those prepared by moist tamping. These findings highlight the critical role of specimen preparation in assessing suffusion susceptibility and erosion behaviour, particularly for soils near the threshold of instability.
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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 | 0.000 | 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".