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Record W1976113839 · doi:10.1142/s0219876205000557

A GENETIC ALGORITHM BASED PROCEDURE FOR AUTOMATIC CRACK PROFILE IDENTIFICATION

2005· article· en· W1976113839 on OpenAlex

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

VenueInternational Journal of Computational Methods · 2005
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPolygon meshConvergence (economics)Finite element methodIdentification (biology)AlgorithmComputer scienceGenetic algorithmMathematical optimizationMathematicsStructural engineeringEngineeringMachine learning

Abstract

fetched live from OpenAlex

This paper presents a genetic algorithm based procedure for automatic identification of crack profiles. In the procedure geometric modeling technique is applied to incorporate crack(s) into the structure under evaluation and a geometric model is generated. The geometric model is then used to generate finite element mesh. In solving forward problems, finite element meshes are adapted based on error estimation to improve accuracy in computed structural responses. Numerical results show that error from solving forward problems can largely slow down GA convergence and significantly affect the accuracy of estimated crack parameters. Mesh adaptation can effectively reduce the error, thus speeding up the convergence and improving accuracy.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.787
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.410
Teacher spread0.384 · 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