Fatigue life prediction for cables in cyclic tension
Bibliographic record
Abstract
Cables are used in civil and mining engineering applications, such as in cable stays for suspension bridges, elevators, construction cranes, mining shovels and draglines. Such cables are mainly used for supporting or hoisting and can be considered to be either stationary or running cables. In supporting functions, cables are subjected to cyclic tension, with the primary mode of failure due to fretting fatigue of individual wires that make up the cables as they abrade against each other. Stationary cables subjected to cyclic tension (tension–tension) fatigue are the focus of this work, and although other researchers have investigated the fatigue life of such cables, there remains much of the cyclic tensile behavior that needs further investigation. The stress condition of several cables such as 19-, 91- and 92-wire strands; independent wire rope core; and the 6 × 19 Seale–independent wire rope core wire ropes was investigated using finite element modeling techniques. A stress-based approach was used with the results of finite element modeling to obtain the fatigue life for these cables. In practice, it would be challenging for a finite element analysis to be carried out each time the fatigue life of a cable is needed, so in a preventive maintenance stance, regression coefficients have been proposed for use with a fatigue life prediction model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".