Assessment of critical parameters affecting fretting fatigue life of bridge stay cables at saddle supports
Bibliographic record
Abstract
Saddle systems are a popular solution for supporting cables in cable-stayed bridges. Fretting fatigue failure of cables at saddle supports is a primary design consideration for these systems. Critical parameters that affect the fretting fatigue life of the cables are the contact forces and the slip displacements at the contact points between the cables and the saddle. Determining these critical parameters is the first step in evaluating the fretting fatigue life of the cables. Wear in the contact points between the cables and the saddle can affect the load distribution in saddle systems and consequently affect these critical parameters. However, the effect of wear has not been studied in the previous works. This paper first discusses the methods proposed in the literature for evaluating contact forces and slip displacements in the contact points between a cable and a saddle. Then, a finite element model of the problem and a framework for modelling the wear are presented. Finally, fretting fatigue life is determined based on the different studied approaches. The main highlights of the current study are considering the effect of wear in the simulation and employing an enhanced FE model for slip displacement calculations. The results of the basic model without wear effects showed that the first contact point between the cable and the saddle is critical for fatigue failure. However, by incorporating wear in the model, the contact force at the first point dropped and the second contact point became critical; this is in line with the observations in large-scale fatigue tests of saddle 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".