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Record W4411501513 · doi:10.1111/ffe.70006

Probabilistic Modeling and Experimental Validation of Fatigue Damage in Riveted Lap Joints of Aircraft Structures

2025· article· en· W4411501513 on OpenAlexaff
Junhua Zhang, Jianjiang Zeng, Hao Qin, Mingbo Tong, Kai Liu, Furui Shi, Nan Sun, Kun Song, Shuo Zhao, Jie Zheng

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Alberta
FundersNanjing UniversityNanjing University of Aeronautics and AstronauticsNational Natural Science Foundation of China
KeywordsStructural engineeringParametric statisticsProbabilistic logicFracture mechanicsLap jointDamage mechanicsMonte Carlo methodEngineeringFinite element methodComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.246
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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