A particulate-scale investigation of cemented sand behavior
Bibliographic record
Abstract
In this paper, triaxial tests and numerical simulations using the discrete element method (DEM) are combined to explore the underlying mechanisms of the unique behavior of artificially cemented sands. The experimental results show that strength enhancement, volumetric dilation, and the shear banding associated failure mode are observed in Portland cement sand; these features become more pronounced with increasing cement content. Different responses are found in gypsum-cemented sand even though both types of cemented sand specimens were prepared under very similar void ratios before shearing. The DEM simulations on the Portland cement sand were carried out under two particular arrangements (i.e., the use of small cementing particles and flexible membrane boundaries). The simulation results reveal that particles in the bonding network jointly share the loading and many micro force-chains associated with cementation are created. Compared with uncemented sand, a more stable and stronger force–chain complex subjected to smaller force concentration is formed in cemented sand, which gives rise to higher strength. Intensive bond breakage, concentrated relative particle movement, column-like force chains, great particle rotation, and high local porosity are found inside the shear band. The bonded clusters remain at large strains to help stabilize the particle arch and therefore to maintain the volumetric dilation.
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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 | 0.000 |
| 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".