A simple and efficient approach to capturing bonding effect in naturally microstructured sands by discrete element method
Bibliographic record
Abstract
Abstract A discrete element modelling of naturally microstructured sands is very important to geomechanics. This paper presents a simple discrete element model for naturally microstructured sands with the aim to efficiently capture the effect of cementation between particles (bonds). First, a simple bond contact model was proposed by introducing a rigid‐plastic bond element into the conventional contact model for dry granular material. Second, efficient numerical techniques were investigated to implement this contact model into the distinct element method (DEM). Then, a two‐dimensional DEM code was developed to simulate a series of isotropic compression tests on the materials of different densities and bonding strengths. Finally, the DEM results were examined in comparison with the experimental data on artificially bonded sands obtained by Rotta et al. (Géotechnique 2003; 53(5):493–502). In addition, we discussed the yielding mechanism, the Coop and Willson criteria on weak/strong bonding (J. Geotech. Eng. (ASCE) 2003; 129(11):1010–1019) and the strong bonding phenomenon observed by Rotta et al. based on the DEM data. The study shows that the DEM model is able to capture the main features of naturally microstructured sands, such as variations of yielding and bulk modulus against bonding strength or material density. In addition, it is shown that the gross yielding (the yielding defined in terms of strains) is largely related to bond breakage; Coop and Willson criteria are generally reasonable; and the strong bonding in the experimental data obtained by Rotta et al. comes from that their bonded materials start at different points on the same compression line. Copyright © 2006 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".