Behaviour And Strength Of High Strength Concrete L-Beams Under Combined Shear, Bending And Torsional Loading
Bibliographic record
Abstract
This investigation examines experimentally the behavior and ultimate strength of L-shaped reinforced high strength concrete beams under combined shear, bending and torsion. The experimental program consists of casting and testing (13) high strength concrete beams under bending, shear and torsion. The main parameters are the effect of compressive strength, the eccentricity and transverse to longitudinal reinforcement ratio. An increase in transverse to longitudinal reinforcement ratio by (21.43%) for beams under 80mm eccentricity of loading causes decrease in ultimate resisting torsional moment by (13.19%), and for an eccentricity of 170mm; the decrease in resisting torsional moment is (15.14%). Increasing in eccentricity for torsion of (112.5%) caused a decrease in load carry capacity by (43.33%) if all types of reinforcement remain the same. By using multiple nonlinear stepwise regression analysis, based on data in this research and from other literatures; equations are proposed for predicting shear strength at cracking and ultimate loads and torsional capacity at cracking and ultimate loads. These proposed equations showed good agreement when compared with equations given by Codes of practice like (ACI, Canadian and BS) and showed good relations.. Keywords: Bending, high strength concrete, L-shaped beams, shear, torsion.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".