Predicting the effect of stirrups on shear strength of reinforced normal-strength concrete (NSC) and high-strength concrete (HSC) slender beams using artificial intelligence
Bibliographic record
Abstract
The exact effect that each of the basic shear design parameters exerts on the shear capacity of reinforced concrete (RC) beams without shear reinforcement (Vc) is still unclear. Previous research on this subject often yielded contradictory results, especially for reinforced high-strength concrete (HSC) beams. Furthermore, by simply adding Vc and the contribution of stirrups Vs to calculate the ultimate shear capacity Vu, current shear design practice assumes that the addition of stirrups does not alter the effect of shear design parameters on Vc. This paper investigates the validity of such a practice. Data on 656 reinforced concrete beams were used to train an artificial neural network model to predict the shear capacity of reinforced concrete beams and evaluate the performance of several existing shear strength calculation procedures. A parametric study revealed that the effect of shear reinforcement on the shear strength of RC beams decreases at a higher reinforcement ratio. It was also observed that the concrete contribution to shear resistance, Vc, in RC beams with shear reinforcement is noticeably larger than that in beams without shear reinforcement, and therefore most current shear design procedures provide conservative predictions for the shear strength of RC beams with shear reinforcement.Key words: analysis, artificial intelligence, beam depth, compressive strength, modeling, shear span, shear strength.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".