Behaviour and modeling of deep beams with high shear span-to-depth ratios
Bibliographic record
Abstract
A study of the response of eight full-scale deep beams was carried out at McGill University. Four beams were tested by Li (2003) and this thesis reports on the testing of the remaining four beams. The deep beams reported in this thesis were 2000 mm long by 400 mm thick. Two beams had an overall depth of 520 mm and the other two beams had an overall depth of 810 mm. Two beams were reinforced with main tension tie reinforcement only, while the other two contained both vertical and horizontal uniformly distributed reinforcement. These beams were tested under concentrated load to investigate the influence of span-to-depth ratio and the influence of uniformly distributed horizontal and vertical reinforcement. The presence of uniformly distributed steel resulted in higher capacities, better crack control and also served to control bond splitting failures near the supports. Four approaches were used to predict the capacities: a plane-section model, a simplified strut-and-tie model, a model based on the 1996 FIP Recommendations and a refined strut-and-tie model. The 1996 FIP (Federation Internationale de la Precontrainte) Recommendations gave conservative predictions suitable for design. The refined strut-and-tie model gave the most accurate predictions due to the fact that this approach accounted for the contributions of both the horizontal and vertical uniformly distributed reinforcement in the strut-and-tie model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".