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Record W4415070987 · doi:10.1016/j.rineng.2025.107666

Fatigue life prediction of beam structures with breathing cracks using finite element analysis

2025· article· en· W4415070987 on OpenAlex
Hui Long, Yilun Liu, Changzheng Huang, Kefu Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResults in Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsLakehead University
FundersShaoguan UniversityNatural Science Foundation of Guangdong Province
KeywordsFinite element methodBeam (structure)BreathingWork (physics)Deformation (meteorology)

Abstract

fetched live from OpenAlex

This study presents a numerical procedure for estimating the fatigue life of cracked beams subjected to two-dimensional (2D) and three-dimensional (3D) direct and/or base excitations. A finite element (FE) model is developed for a cracked beam, in which the stiffness matrix of the beam element containing a breathing crack is formulated based on its strain state. Using this model, the stress intensity factor is computed for each vibration cycle, accounting for the breathing behavior of the crack. Fatigue crack growth increments are then evaluated using Walker’s equation to determine the total crack growth life and the maximum stress intensity factor. The fatigue status of the beam is assessed using three defined failure criteria. The proposed computational procedure is validated through two case studies involving a simply supported beam and a fixed-fixed beam, both containing breathing cracks. The maximum prediction error compared with experimental results is 7.91 %, demonstrating the method’s high accuracy. This work provides an effective and generalizable FE-based framework for fatigue life prediction of dynamically loaded beam structures with breathing cracks.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.017
GPT teacher head0.278
Teacher spread0.260 · 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