Bibliographic record
Abstract
The objective of this research work is to assess existing models for different loading conditions. The approach is based on the experimental research findings of the Society of Automotive Engineers (SAE), which is used as a benchmark. The models are classified into three categories: (i) Empirical formulas based on modification of the Coffin-Manson equation; (ii) Critical plane models which are based on physical observation that fatigue crack initiate and grow on certain planes; (iii) Damage mechanics models based on energetic thermodynamic approach. The first two categories of models are implemented in commercial software "FE-Fatigue". The software uses the results of linear finite element analysis to determine the critical locations. The fatigue life prediction is therefore evaluated based on the stress-strain distribution obtained from the finite element analysis. The continuum damage mechanics approach uses a fundamental formulation based on energetic thermodynamics. This technique integrates an elasto-plastic-damage locally coupled constitutive law in the critical location where plastic deformation is accumulated. Based on its nature the continuum damage mechanics approach is restricted to low cycle fatigue. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .B56. Source: Masters Abstracts International, Volume: 42-01, page: 0283. Adviser: Faouzi Ghrib. Thesis (M.A.Sc.)--University of Windsor (Canada), 2002.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".