Canadian design standard for slender reinforced concrete columns
Bibliographic record
Abstract
Most columns are termed as short columns and fail when the material reaches its ultimate capacity under the applied loads. However, columns are subjected to moment as well, with the increase of length (on the trend to be slender), there is a possibility that columns fail due to lateral deflection. For the procedure of column design, axial loading is firstly treated. Methods are then given for design of sections subjected to both axial loads and moment. \nAcceptable design methods to provide a reliable, economic and safe solution are very important. The purpose of this project is to evaluate several kinds of design methods and to recommend the suitability of each kind of method under different conditions. \nIn this project, Canadian Design Method CAN/CSA-A23.3 (2004) is of the major interest and used to predict the failure loads of 150 slender reinforced high strength concrete columns. The results are then compared with predictions from ACI318 (2008), EC2 (2004), BS8110 (1997), P-Delta method, Transformation method and experimental data. It can be concluded that CAN/CSA-A23.3 (2004) generally predicts lower failure loads of columns and is more conservative than the rest of other five prediction methods, however, CAN/CSA-A23.3 (2004) is not applicable for prediction of very slender column, where slenderness ratio is greater than 100, for which second-order analysis is required. Columns with lower initial eccentricity/depth ratio have faster decreasing rate of failure loads than columns with higher initial eccentricity/depth ratio. In the case that slenderness ratio is between 30 and 60, ACI318 (2008) and Transformation method predicts the most accurate columns’ failure loads. When slenderness ratio is in the range from 60 to 100, BS8110 (1997) predicts the most accurate results. The results predicted form P-delta method and Transformation are generally quite close.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.024 |
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".