Bibliographic record
Abstract
The technique of homogenization is important in modeling. It allows the scope of the numerical problem to be reduced, thus making the analysis computationally more efficient and allowing the engineer to focus on important larger scale features that influence overall behaviour rather than getting caught up in details that can also lead to numerical difficulties. This paper begins by demonstrating using photoelasticity the importance of taking into account layering details. The inability to capture stress variations due to homogenization is also demonstrated by comparing finite element solutions that take into account details with those which do not. The paper then investigates the consequences of homogenizing a layered system to express its stress–strain response in terms of an equivalent homogeneous anisotropic medium. This is accomplished by analyzing via the finite element method an idealized layered system and comparing the averaged constitutive relation from the numerical solution with that corresponding to an equivalent homogeneous transverse isotropic medium. Thereafter, stress and failure patterns corresponding to a structured medium are examined, as are the consequences of free surfaces and interfaces between layers on the nonhomogeneity of failure.Key words: homogenization, layered soils, photoelasticity, finite element analysis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".