Behavior of Rectangular Columns Constructed with SCC and Steel Fibers
Bibliographic record
Abstract
Extensive research has shown that properly detailed and closely spaced transverse reinforcement in reinforced concrete columns can ensure ductile behavior during earthquakes. However, in regions of high seismicity, detailing requirements can result in heavily congested sections; the use of self-consolidating concrete (SCC) can facilitate construction in these situations. Although extensive research exists on the axial load behavior of traditional concrete columns, only limited research exists on the behavior of columns constructed with SCC. Research over the past two decades has also shown that use of steel fiber-reinforced concrete (SFRC) can improve the strength and ductility of columns by delaying cover spalling and improving core confinement. Recent research has also shown that the combined use of SCC and steel fibers can ease problems associated with the workability of traditional fiber-reinforced concrete. This paper presents the results from an experimental program that was conducted to study the axial behavior of reinforced concrete columns constructed with SCC and SFRC. Full-scale columns having rectangular cross sections were tested under pure axial compressive loading. The columns were detailed with varying amounts of transverse reinforcement in accordance with the requirements of the Canadian Standards Association. The results confirm that increasing confinement with closely spaced transverse reinforcement in rectangular SCC columns results in improvements in behavior and ductility. In addition, the results demonstrate that the use of SFRC in rectangular columns results in improvements in performance and ductility.
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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 | 0.000 |
| 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) | 0.001 | 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".