Numerical and experimental validation of applicability of Froude's similitude modelling for RC beams
Bibliographic record
Abstract
Experimental investigations are crucial in civil engineering, particularly in understanding complex structural behaviours. A common challenge faced by these investigations is linked to resource limitations, making full-scale testing often impractical. This challenge can be addressed by applying similitude theory, allowing for scaled-down experimental tests. This approach, however, has its limitations. Traditional similitude techniques for small-scale modelling may not accurately replicate full-scale behaviours or require special equipment like centrifuges, impacting cost-effectiveness. This paper is the first to propose addressing the observed inaccuracies using Froude similitude modelling for reinforced concrete (RC) beams. This innovative approach overcomes the limitations of traditional similitude techniques and offers a more cost-effective and practical solution for replicating full-scale behaviours. This modelling technique is first examined numerically. Then, suitable materials for small-scale models were identified and utilized to fabricate small-scale RC beams. The physical tests on the small-scale model beam and the equivalent full-scale prototype beam assured the validity of the scaling method.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".