Structural health monitoring of a dragline cluster using the hot spot stress method
Bibliographic record
Abstract
‘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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".