Performance Analysis of Recycled and Natural Aggregate Concrete Column with Varying Design Parameters
Bibliographic record
Abstract
An analytical approach is made to show the performance of recycled aggregate concrete (RAC) columns with varying design parameters and to compare these with natural aggregate concrete (NAC) columns. The design parameters taken into consideration include concrete compressive strength, steel yield strength, longitudinal reinforcement ratio, and applied axial load. These factors were considered for two different aspect ratios which ensure flexural failure behaviour of column. A two-level factorial analysis was performed, and the columns were modelled and analysed using SeismoStruct, a finite element analysis software. The observed responses include: base shear capacity and displacement at first cracking; first yielding of steel; first crushing of concrete; and the ductility of the column. The pushover analysis was used to determine the performance of each column and statistical software R was used for the analysis of variance (ANOVA), which determines the percent contribution of each design parameter and their interactions on various performance criteria. The analysis shows that, RAC columns perform with improved ductility compared to NAC column.
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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) | 0.002 | 0.001 |
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".