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Record W1984739337 · doi:10.1515/hf.2005.106

Effect of stress levels on compressive low-cycle fatigue behaviour of softwood

2005· article· en· W1984739337 on OpenAlexafffund
Meng Gong, Ian F. C. Smith

Bibliographic record

VenueHolzforschung · 2005
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCreepMaterials scienceCompression (physics)Composite materialCyclic stressModulusStress (linguistics)Strain (injury)Compressive strengthStructural engineering

Abstract

fetched live from OpenAlex

Abstract Low-cycle fatigue (LCF) of spruce under parallel-to-grain compression was investigated to simulate the damage that occurs during extreme events such as hurricanes. Load control was used, with peak stress levels of 75%, 85% and 95% of static compressive strength ( C max ). Changes in the residual cyclic modulus, cyclic creep strain and modified work density were correlated with the number of load cycles to assess their suitability as damage indicators. Creep tests were also carried out and the strain compared with cyclic creep strain under LCF load. Fatigue and creep tests had a total duration of 10 min. A three-element mathematical model was used to predict the cyclic creep strain. Some key findings were that: (1) the residual cyclic modulus varies with the number of load cycles at a given stress level and decreases with an increase in stress level; (2) cyclic creep strain and pure creep strain are strongly influenced by the peak stress level; and creep specimens fail but fatigue specimens do not at a 95% peak stress level; and (3) the three-element mathematical model is appropriate for predicting cyclic creep strain.

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 categoriesnone
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 score0.867
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2005
Admission routes2
Has abstractyes

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