Micro Characterization of Deformations in Granular Materials during Shear
Bibliographic record
Abstract
One of the greatest challenges in studying the engineering behavior of granular materials is the lack of tools for an unobstructed view of a large quantity of microscopic particles. This dissertation presents a thorough characterization of deformations in sheared granular materials. The investigation involves experimental programs, utilization of computed tomography (CT) techniques, and application of Distinct Element Method (DEM). The investigation was conducted at multi-scale (macroscopic, mesoscopic, and microscopic) to better understand the physical properties and constitutive behavior of granular materials during shear. The experiments include conducting a series of axisymmetric triaxial, miniature axisymmetric triaxial, and biaxial experiments. They were focused on dense and dry specimens tested under low confining pressures. CT was used to acquire three-dimensional (3D) images of specimens to characterize particles properties and interaction at the mesoscopic and microscopic levels. Two types of CT systems were utilized; an industrial CT system capable of scanning relatively large specimen with medium resolution, and Synchrotron Micro-Tomography (SMT) system, capable of scanning smaller specimen with much higher resolution than industrial CT system. DEM algorithm written in Particle Flow Code in 3D was employed to simulate the laboratory experiments at a wide range of confining pressure levels. Two types of granular material were used; F-75 Ottawa sand and Johnson Space Center (JSC-1A) lunar regolith simulant. Results include particle micro-characterization, spatial void ratio distributions, quantification of shear band thickness, elucidation of localization phenomenon, particle contacts, and fabric evolution and development of stress-dilatancy empirical models. The main findings include: 1) spatial variation of void ratio in a test specimen is caused by localization effect, membrane confinement, and confining pressure; 2) the interface regions near shear bands were introduced and thickness calculation procedure was proposed; 3) good correlations were found between average particle contacts and void ratio; 4) the majority of particles within the shear band orient in the x-y plane, whereas particles outside the shear band have no preferred orientation; 5) empirical models was developed to predict the peak friction and the dilatancy; 6) DEM was capable of predicting the friction angles at very low confining pressure under terrestrial and reduced gravity environments.
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 | 0.000 |
| 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".