Proposed prediction models for shear strength of fiber reinforced polymer reinforced concrete deep members without stirrups
Bibliographic record
Abstract
Arch action in deep reinforced concrete (RC) members has a beneficial effect on shear capacity. The Strut-and-Tie Method (STM) is one of the proposed methods for the design of steel reinforced deep beams (DBs). However, some iterations may require to obtain the optimum solution. This paper investigates the shear capacity of fiber reinforced polymer (FRP)-reinforced DBs using STM and sectional methods of Canadian Standard Association (CSA) and American Concrete Institute (ACI) design provisions. To this end, 106 FRP-reinforced DBs were compiled from the literature. It has been found that current sectional methods do not adequately account for the effects of arch. action on DBs. From this investigation, modifications were proposed in the current sectional methods to calculate the shear capacity of FRP-reinforced DBs. The proposed modifications were found to significantly improve the prediction accuracy. The sectional methods proposed by CSA and ACI were found to be better than the CSA-STM method in predicting the shear capacity of FRP-reinforced DBs.. The mean, standard deviation and coefficient of variation for the proposed CSA sectional method are 1.00, 0.28 and 28.2% and for the proposed ACI sectional method are 1.01, 0.26 and 25.6, respectively. The same for the CSA-STM method are 2.20, 0.76 and 34.4%, respectively. The proposed methods can be used to predict the shear capacity of FRP reinforced deep members.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".