Bibliographic record
Abstract
Probabilistic analyses of the modeling errors of several selected strength prediction models for slender reinforced concrete (RC) columns are carried out. The selected strength models include the moment magnifier methods that are recommended in the ACI and CSA design codes and the stability-based theoretical model. A relatively large amount of test data on slender RC columns is collected from the literature and the test results are compared with the ones obtained from different strength prediction models. Both normal- and high-strength concrete columns are included in this study. Probabilistic analyses of the modeling error include the use of pseudolikelihood estimation method. Analysis results suggest that the coefficient of variation of the modeling error for slender RC columns can be as high as 20%, which is considerably larger than those suggested and employed for reliability analysis in the literature. The results also suggest that the modeling error for slender RC columns depends on concrete compressive strength, the load eccentricity, and the slenderness ratio. However, the effect of the slenderness ratio on the modeling error is negligible. Sets of probabilistic models of the modeling errors by considering different strength models for slender RC columns are suggested.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".