Numerical Prediction of the Fatigue Life of Complex Riveted Structures
Bibliographic record
Abstract
In this study, a numerical prediction methodology used to evaluate the fatigue life of complex riveted aluminum alloy structures subjected to variable amplitude loads is presented. This methodology is based on the combination of experimental fatigue tests with the structural stresses approach to generate S(N) curves. Single-riveted specimens (Al5052-H36) with different characteristics (rivet diameter, sheet thickness, assembly configuration) were first tested experimentally. Using a simplified finite element model (FEM) and a probabilistic model to compute the structural stress of these tested samples, fatigue curves for each type of failure encountered during testing (sheet metal and rivet) with a confidence interval were generated. Of the probabilistic models that were studied, the Stüssi model was the most effective to correlate the experimental results. The proposed methodology was then combined with Miner’s law to predict the fatigue life of complex riveted structures subjected to variable amplitude loading. Using the proposed methodology, satisfactory predictions of the fatigue life of multi-rivet specimens and a structural assembly from a recreational vehicle subjected to variable amplitude loads were obtained without the need to use a complex finite element model for the riveted joints. The methodology proposed in this paper is efficient and quick to use, can be used for various states of stress, and is well suited for structural or fatigue optimization problems.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 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".