Service loads in dragline tubular structures: a case study of cluster A5
Bibliographic record
Abstract
Draglines are used extensively for removal of overburden in the coal mining industry. Draglines with tubular booms are among the structures most susceptible to fatigue cracking due to the large number of high load cycles to which they are subjected during operation. Circular hollow section tubes are used as both lacing and chord members. In this paper, a study was carried out to better understand the stresses in a 4-lacing cluster during operation. Strain gauges were installed on a typical dragline cluster A5 to measure strains generated while in operation. Static and dynamic (swing and digging) tests were carried out, and strains obtained during the different tests were used to calculate both nominal stresses and hot spot stresses. For cluster A5, the hot spot stresses at weld toes in the lacing members were found to be significantly larger than those at weld toes in the chord members. Bending stresses were found to form a relatively larger portion of the nominal stresses at the weld toes in the lacing members compared to chord members. The results of this work highlight a conclusion found in the authors' previous work that the high tensile residual stresses resulting from welding are an important issue not measured in hot spot stress testing, but these stresses are relevant to the levels and location of cracking observed in practice. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".