Probabilistic Modeling and Experimental Validation of Fatigue Damage in Riveted Lap Joints of Aircraft Structures
Bibliographic record
Abstract
ABSTRACT The unpredictability of crack initiation and propagation in aircraft structures with multiple site damage (MSD) and widespread fatigue damage (WFD) presents significant challenges for maintaining the structural integrity of aircraft under fatigue loading. This paper presents a probabilistic analysis model for riveted lap joints with MSD. The probabilistic analysis model leverages a secondary customization of ABAQUS, enabling parametric modeling of penetration cracks with varying lengths. In this model, the stochastic processes of crack initiation, propagation, and failure are simulated through a Monte Carlo framework, incorporating the theories of fracture mechanics and fatigue statistics. In addition, a group of riveted lap joint tests are carried out to verify the accuracy of the calculation results. The simulated results are in good agreement with the mean experimental fatigue life. However, the model underestimates the dispersion observed in the experimental data in the current study, primarily due to the lack of extensive experimental data needed for further calibration. Overall, the developed model can capture the complex, interdependent mechanisms of fatigue damage in riveted lap joints with MSD.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".