An Estimate Method of EPFM Constraint Parameter in 3D Cracked Structures for Sensor Structure Design
Bibliographic record
Abstract
An estimating method, which conveniently and quickly predicts solutions of constraint parameter A in J-A two-parameter approach of elastic-plastic fracture mechanics (EPFM), was developed and successfully applied on three-dimensional (3D) cracked structures under both uniaxial and biaxial loading condition. The method (estimate formula) forA value estimate was developed theoretically first. Then, based on the obtained numerical solutions of parameter A from finite element analysis (FEA), the coefficient values of the proposed estimate formula were determined for 3D single edge cracked plate (SECP) structures under both uniaxial and biaxial loading, to estimate solutions of parameter A for 3D SECP. Through comparing predicted parameter A values with their FEA numerical solutions from authors and other researchers, it is validated that the proposed estimating method can be used well to predict A values for thin 3D cracked structures. It enables the application of the EPFM J-A two-parameter approach on practical engineering structure analysis and design of sensor and other mechanical systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".