Non-linear finite element analysis and parametric investigation of low-rise reinforced concrete shear walls.
Bibliographic record
Abstract
A parametric study was carried out on 18 reinforced concrete shear walls to investigate the significance of design parameters on seismic response. The parameters included boundary elements and their reinforcement characteristics, web reinforcement ratios, concrete strength, steel yield strength, and axial load. The investigation was conducted using finite element analyses. Computer program ADINA was used to perform nonlinear analysis of concrete shear walls under axial load and incrementally increasing lateral forces. Material nonlinearities, including those of concrete, were modeled using ADINA material models. Four-node isoparametric plane stress elements were used to model concrete. Steel elements were modeled using two-node nonlinear truss elements. The applicability of the program and the accuracy of finite element models were first validated. This was done by comparing the results of six different shear walls with those obtained experimentally. The walls included three specimens tested by Maier at the Swiss Federal Institute of Technology in Zurich, Switzerland and three shear walls tested by Lefas et al. at Imperial College of Science and Technology, London, England. The analytical and experimental results showed very good agreement. The results of the parametric investigation indicate that the presence of boundary elements enhanced strength and deformability of shear walls significantly. Wall aspect ratio was also found to be an important parameter dictating the mode of behavior. The longitudinal reinforcement ratio in the boundary elements was found to be a good source of energy dissipation and had much greater influence on ultimate load capacity than the web reinforcement. When confinement of concrete was considered, both the ultimate load capacity and ductility of shear walls improved. The effects of increased axial compression and increased reinforcement yield level were to increase strength but reduce 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".