Effect of sand particle size on interface shear behaviour between bio-cemented sand by MICP treatment and steel structure
Bibliographic record
Abstract
This study presents an experimental investigation of the effect of sand particle size on the shear behaviour of bio-cemented sand-steel structure interface, with sand treated by microbially induced calcite precipitation (MICP). Five sand groups were involved with different median particle sizes. The MICP treatment followed the surface percolation method featuring bacterial suspensions with a fixed optical density (linearly related to active cell concentration or urease activity). Scanning electron microscopy was used to identify the microstructure of the samples, while interface shear test was performed to observe the macroscale mechanical behaviour. The testing results indicated that smaller sand particle size was associated with a relatively larger effective calcium carbonate bonding area, providing a more significant bonding effect. Thus, smaller sand particle sizes in the treated samples were associated with higher values for the interface cohesion and a more pronounced increase in the interface friction angle at the peak state (compared to the corresponding untreated samples). By contrast, because the bonding effect broke close to the interface during shearing, the bottom of the treated samples became planar and smooth. Hence, a lower interface friction angle at the ultimate state was identified for a treated sample, compared to the corresponding untreated one.
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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 | 0.000 |
| 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".