Non-Linear Analysis of Concrete Deep Beams Reinforced with FRP
Bibliographic record
Abstract
Deep beams are the elements that have applications in many structures such as pile caps, girders, foundation walls and offshore structures. The behaviour of deep beams is mostly affected by its depth-span ration, type of reinforcements, materials used in the concrete. Since the behaviour of deep beams are different than the traditional beams, a few conventional methods has been introduced to model these types of beams such as empirical equations or strut and tie model (STM). However, there is a lack of study on the modelling and designing deep beams reinforced with the FRP reinforcement in the conventional methods. In this paper four deep beams reinforced internally with GFRP has been modelled experimentally and numerically. The behaviour of the beam has been recorded during the experiment and it has been compared with a non-linear numerical model to verify the results. The results of the ultimate loads, and load-deflection of the beams has been compared and verified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.000 | 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 teacher head, 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".