Mechanical Characterization of Granular Materials for Silos Design Using Geotechnical Experiments
Bibliographic record
Abstract
This investigation focuses on the experimental determination of critical mechanical properties of various granular materials, namely cement, sand, wheat, granulated sugar, and wheat flour, which are essential in the design of silos.These properties, crucial for defining the behavior of materials stored in silos through numerical methods, have been less documented in existing literature.Conducted at the Research Laboratory of Civil Engineering and Hydraulics of 8 May 1945 University, Guelma, Algeria, a series of geotechnical tests, including triaxial, ring shear, and Oedometric tests, were employed to ascertain a range of properties: internal friction angle, apparent cohesion, specific weight, elastic modulus, Poisson ratio, particle-to-steel wall friction coefficient, and dilatancy angle.It was observed that sand and sugar exhibit the highest rigidity, as indicated by their elastic moduli.In terms of compressibility, sugar, wheat, and wheat flour demonstrated the greatest extent.Discrepancies between the results of triaxial and shear tests suggest a probable influence of material consolidation methods on outcomes.While certain findings aligned with previous studies, discrepancies in materials like cement and sand necessitate additional testing for verification.This study proposes recommended values for these mechanical properties, contributing significantly to the field of silo design.
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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 | 0.001 | 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".