Effect of stress levels on compressive low-cycle fatigue behaviour of softwood
Bibliographic record
Abstract
Abstract Low-cycle fatigue (LCF) of spruce under parallel-to-grain compression was investigated to simulate the damage that occurs during extreme events such as hurricanes. Load control was used, with peak stress levels of 75%, 85% and 95% of static compressive strength (Cmax). Changes in the residual cyclic modulus, cyclic creep strain and modified work density were correlated with the number of load cycles to assess their suitability as damage indicators. Creep tests were also carried out and the strain compared with cyclic creep strain under LCF load. Fatigue and creep tests had a total duration of 10 min. A three-element mathematical model was used to predict the cyclic creep strain. Some key findings were that: (1) the residual cyclic modulus varies with the number of load cycles at a given stress level and decreases with an increase in stress level; (2) cyclic creep strain and pure creep strain are strongly influenced by the peak stress level; and creep specimens fail but fatigue specimens do not at a 95% peak stress level; and (3) the three-element mathematical model is appropriate for predicting cyclic creep strain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".