The analysis of numerical self-compacting concrete wall panel models with variations of shear reinforcement
Bibliographic record
Abstract
Reinforced concrete wall critical zones are the responsive areas of dissipated earthquake loads. They are formed in the connection of the wall panels and the fixed restraints. The longitudinal and transversal steel reinforcements with certain spacing are designed according to the required nominal strength at the connections. Under certain conditions, the reinforcement distance becomes very tight, making working on castings using normal concrete difficult. This condition also occurs in boundary elements consisting of longitudinal and transversal reinforcements in tight spaces. A concrete material that flows easily and solidifies itself is required to avoid segregation. One type of this material is Self-Compacting Concrete (SCC). The SCC performance as a wall panel material that withstands gravity and cyclic lateral loads still require further research. This study aimed to analyze the hysteretic performance of reinforced SCC wall panels with variations of shear reinforcement in resisting cyclic lateral loads. The analysis used software based on numerical analysis. The drift ratios, hysteretic curves, stress patterns, ductility, and stiffness of the wall panels were analyzed. The SCC wall panel with ordinary shear reinforcement resisted lateral positive and negative loads of 152.32 kN and 143.09 kN, respectively. In comparison, the wall panel with boundary elements and tighter shear reinforcements could withstand the positive and negative lateral loads of 187.62 kN and 145.98 kN, respectively. The SCC wall panel reached the best ductility of 21.38 with ordinary shear reinforcement because the yield occurred faster than in other wall panels. The results showed that the boundary elements and shear reinforcements of reinforced SCC wall panels affected the performance in resisting cyclic lateral loads.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".