THE COMPARATIVE ANALYSIS OF THE RESULTS OF REAL AND NUMERICAL EXPERIMENTS FOR DEFINING THE ULTIMATE BEARING CAPACITY OF LIGHT GAUGE STEEL STUDS “ATLANT”
Bibliographic record
Abstract
The article in question describes the results of comparison of numerical and real experiments, presented in the report of the Canadian laboratory “Bodycote” [1]. The paper depicts the test fixture, on witch six samples (two typical sizes) of light gauge steel stud “ATLANT” were tested. As a result, dependencies between bearing capacity and characteristic displacements were received. In order to repeat the experiment, the series of computer models were created with the software ANSYS. The computer models have complex nature: geometrical / physical nonlinearity was used. For accounting steel material nonlinearity, we used a multilinear model with isotropic hardening (MISO). The proper diagram was used for each typical size in accordance with the experiments on the tensile-testing machine. For the purpose of supporting condition and load modeling which are identical to the full-scale experiment, a couple of contact elements were used. The utilization of contact elements allowed us to consider friction between the sample and the supporting structures. One of the disadvantages of the full-scale experiment is the absence of measurements of the initial geometrical imperfections. For their consideration the use of probabilistic approach is suggested. This approach entails calculation of several models with different spread of initial imperfections. The initial geometrical imperfections with stochastic nature were included in the computing model. Parameters of distribution were based on the measurements of eighty-eight C-shaped members [8]. In the result of comparison, fine precision in terms of the ultimate bearing capacity and deformation pattern were established. According to expectation, the results of the full-scale experiments were found inside the fictitious “corridor”, created in accordance with the results of computer modeling.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".