Experimental and numerical study on the load-bearing capacity, ductility and energy absorption of RC shear walls with opening containing zeolite and silica fume
Bibliographic record
Abstract
This article originates from an experimental program and nonlinear finite element analysis aimed at examining how pozzolanic concrete influences the behavior of RC shear walls with openings. To achieve this, stress-strain diagrams, and the elastic modulus of 33 cylindrical concrete specimens, each containing varying percentages of silica fume and zeolite (ranging from 0% to 25%), were evaluated. The impact of silica fume and zeolite pozzolans on the ductility and load-bearing capacity of RC shear walls with openings was explored. This was done by analyzing the mechanical properties of the specimens and integrating them into the analytical model. Subsequently, a shear wall with conventional concrete was simulated using the finite element method (FEM). The study delved into the effects of substituting the initial concrete with pozzolanic concrete within the shear wall. Additionally, it investigated the simultaneous reduction in the diameter of the reinforcing bars employed, all in the pursuit of attaining the optimal design for these walls. The findings demonstrated that employing pozzolanic concrete in shear walls, coupled with a balanced configuration of rebar, led to heightened ductility, improved energy absorption, and an enhanced load-bearing capacity for the walls.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".