A unique crack growth rate curve method for fatigue life prediction of steel structures
Bibliographic record
Abstract
In this paper, a unique crack growth rate curve method, which is based on the equivalent stress intensity factor range (ESIFR) as the driving force, has been proposed and examined with crack growth rate data of base metals and as welded joints of some structural steels under constant amplitude external loading. By expressing the crack growth rate data with ESIFR instead of stress intensity factor range (SIFR) make it possible to establish a concise model for crack growth data under different R-ratios to the curve corresponding to R=0 both for base metals and welded joints. The most commonly tested crack growth rate constants under R=0 ∼0.1 are sufficient in fatigue crack growth life prediction of components subjected to tensile-tensile, tensile-compressive loading. Only two equations, one for Mean curve, and the other for Mean + 2SD curve replace the recommended crack growth rate curves in BS7910 for most structural steels. The phenomena that crack growth rates of as-welded joints under different applied loading ratios behaves independent of the applied loading ratio can be explained and the crack growth rate in residual stress field can be predicted well by the present model. The unique crack growth rate curve method does not only allow us to estimate the fatigue life of specimens of base metal and weld joints, but also the fatigue life of structural components under complex loading conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".