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Record W2171903557 · doi:10.1177/1475921710373293

Structural health monitoring of a dragline cluster using the hot spot stress method

2010· article· en· W2171903557 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

VenueStructural Health Monitoring · 2010
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsConcordia University
FundersAustralian Research Council
KeywordsHot spot (computer programming)Structural engineeringWeldingStrain gaugeSpot weldingFinite element methodCrackingStress (linguistics)BoomFatigue crackingEngineeringForensic engineeringMechanical engineeringMaterials scienceComposite materialComputer science

Abstract

fetched live from OpenAlex

‘Hot spot stress’ is an approach often used to consider fatigue loadings in heavily welded tubular joints. This article reports the determination of hot spot stresses in mining dragline booms, which are often ≥100 m in length, using strain gage measurements and finite element analysis (FEA) modeling as part of a structural health monitoring concept. Strain gages were installed on a typical A11 cluster for estimating hot spot stresses, as recommended in the existing fatigue design guidelines by the International Institute of Welding (IIW) and the International Committee for the Development and Study of Tubular Construction (CIDECT). The results from the experimental measurements and the FEA were found to be comparable to a large measure. It was concluded that while hot spot stresses were high enough at the weld toes to cause cracking, they could not explain the cracking that occurs at the welds in the main chords on their own. Issues in comparing theoretical and experimental measurements are discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.353
Teacher spread0.318 · 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