Prediction of effective confinement pressure in high-strength concrete columns
Bibliographic record
Abstract
High-strength concrete (HSC) columns can achieve a ductile behaviour when sufficiently confined by high-strength lateral steel reinforcement. The full potential of high-strength steel, however, may not be achieved in lightly confined HSC columns, since the stress in the confining steel may not reach the expected yield stress usually assumed in design. Different approaches that determine the actual stress in the confining steel at peak stress of confined concrete for prismatic or cylindrical columns have been developed, namely (i) an iterative approach, and (ii) a direct approach assuming an equivalent circular column concept. In order to eliminate some of the complexities and assumptions used in these models, a new direct and simple approach is proposed. It is based on the compatibility of strains and equilibrium of forces in the column cross-section, and on actual deformations measured in the confining steel of 50 large-scale HSC columns made with widely different concrete strengths, steel yield stresses and confinement pressures. The proposed model shows that the effective stress in the confining steel does not exceed 400 MPa in lightly confined HSC columns. In this case, the use of high-strength steel for the confining reinforcement is not necessary. On the other hand, in sufficiently confined HSC columns, much higher lateral stresses can be achieved if high-strength steel is used with yield stresses up to 800 MPa, ensuring a very ductile load-carrying behaviour. Predictions of the peak stress and peak strain of 50 large-scale confined HSC columns showed a very good agreement with the experimental results.
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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 | 0.001 | 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".