Influence of rubber content on enhancing the structural behaviour of beam–column joints
Bibliographic record
Abstract
This investigation aimed to determine the optimum percentage of crumb rubber (CR) in self-consolidating concrete (SCC) to enhance the structural behaviour of beam–column joints under reverse cyclic loading. The investigated mixtures were developed with percentages of CR ranging from 0% to 25%. The beam–column joint contained 2% flexural reinforcement and 0·6% shear reinforcement. The structural behaviour of the tested beam–column joints was evaluated based on load deflection, initial stiffness, rate of stiffness degradation, failure mode, cracking behaviour, displacement ductility, brittleness index, energy dissipation, first crack load and load-carrying capacity. The results indicated that the optimum percentage of CR to be used in beam–column joint mixtures is 15%. Although using this percentage slightly reduced the load-carrying capacity, it greatly enhanced the ductility, brittleness index, deformability and energy dissipation. The results also showed that further increase in the percentage of CR (above 15%) changed the failure mode of the tested specimens and limited the deformation capacity, which negatively affected the ductility, brittleness index and energy dissipation.
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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 | 0.001 |
| 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) | 0.001 | 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".