Flexural Stiffness Reduction Factor of Reinforced Concrete Column with Equal L-Shaped Section
Bibliographic record
Abstract
The reduced stiffness method had been adopted to consider material nonlinearity characteristics of reinforced concrete structures in concrete structures standards in the United States and Canada. The concrete structures design code of China also accepted the reduced stiffness method as a supplementary method of considering the second-order effects problem when necessary. However, the concrete structures with specially shaped columns code of China still uses amplified coefficients of eccentricity to consider nonlinearity characteristics of reinforced concrete structures with special shape columns. The flexural stiffness reduction factor of reinforced concrete columns with specially shaped sections considering characters of material nonlinearity and geometrical nonlinearity lacks corresponding research. Based on the numerical integral method, the change law of flexural stiffness for existing test models of reinforced concrete columns with specially shaped sections under different axial load levels and different levels of seismic action is analyzed, and results are reported. It is concluded that the theoretical values are in agreement with the test values. As a result, a flexural stiffness reduction factor is proposed to consider characteristics of material nonlinearity and geometrical nonlinearity of reinforced concrete columns with equal L specially shaped sections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".