Performance of Composite Shear Walls Subjected to In-plane Cyclic Loading - A Numerical Parametric Study
Bibliographic record
Abstract
An RC shear wall (wall1), a composite shear wall composed of a single external steel plate connected to a concrete panel (wall2), a composite shear wall constructed from two external steel plates connected to an internal concrete panel (wall3) and finally a composite shear wall fabricated with a single internal steel plate embedded within a concrete panel (wall4) are considered in this study and their behavior are assessed and compared under the effect of an in-plane cyclic load. Variation of the three functions include shear load capacity, energy absorption and shear stiffness of the walls are evaluated numerically using the ABAQUS finite element software. The performance of numerical models is validated against to the experimental results. The effects of four parameters consisting of compressive strength of concrete, yield strength of steel plate, height-to-length ratio of the wall and the thickness of the steel plate are investigated on the above-mentioned functions. Obtained results show that the wall4 has the best performance among all four types of shear walls. For instance, the energy absorption capacity of the wall4 is approximately two times greater than that of wall1 and wall2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".