Assessment of Shear Strength Models of Reinforced Concrete Columns
Bibliographic record
Abstract
Shear strength is a crucial parameter in designing Reinforced Concrete (RC) columns considering the effects of lateral loads such as wind or earthquakes. Numerous design codes and published studies have proposed equations for calculating the shear strength of RC columns. However, a discrepancy exists between the calculated models and the experimental results. The aim of this study is to evaluate the calculated models for the shear strength of rectangular RC columns based on 735 data sets, obtained from the literature. Six code-based and empirical models are investigated in this paper. The four code-based models include ACI 318 (2014), CSA (2014), Eurocode 8 (2005), and FEMA 273 (1997), and the two empirical models are proposed by Ascheim & Moehle (1992) [8] and Sezen & Moehle (2004) [9]. The shear strengths of RC columns are calculated for the six models using inputs from the experimental database. Finally, the results are evaluated using statistical indicators, including coefficient of determination and root-mean-squared error. The results reveal that Eurocode 8 (2005) is the best model, followed by Sezen & Moehle (2004) and Canada CSA (2014) since the results of those models are close to the experimental ones and shown to be more conservative than the others.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".