Experimental Study of Enhancing the Shear Strength ofHidden/Shallow Beams by Using Shear Reinforcement
Bibliographic record
Abstract
The primary objective of this article is to study the effect of shear reinforcement on the performance of wide shallow beams. The investigated parameters include the crack patterns, mode of failures, load-deflection curves, load-strain curves of stirrups and the failure load. Nine tested specimens have 1800mm clear span and 500mm width with different thicknesses (150mm, 200mm, and 250mm), and type of the web reinforcement. The experimental results showed that there was a significant improvement in the shear strength due to using the traditional tied stirrups for beams with depth 250mm, but it seems that the vertical tied reinforcement does not work properly to resist the shear for beams with depth less than 250mm. While the welded link web reinforcement increases the shear capacity for beams with depth less than 250mm. A comparison between experimental shear capacities and the prediction of the ECP-203-2016, ACI 318-14, EN1992 and CSA 2004 codes are also presented in this research. It is recommended to re-evaluate the contribution of the shear reinforcement according to the Egyptian ECP-203. In addition, the tested beams specimens are analysed using the nonlinear finite element method (ANSYS). The results showed that the effect of web reinforcement on improving shear strength is more pronounced at higher depth of specimens.
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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 | 0.000 |
| 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 | 0.000 |
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".