A Study on Pushover Analysis of Frame Structure Infilled with Low-Rise Reinforced Concrete Wall
Bibliographic record
Abstract
Abstract This paper focused on the pushover analysis of a frame structure infilled with low-rise reinforced concrete (RC) wall. The softening model of concrete as well as the elastoplastic model of reinforcement was considered in the analysis associated with the equilibrium and compatibility conditions. Accordingly, the shear load-deformation relationship of the wall subjected to monotonic lateral load can be analyzed through the proposed analysis procedure. Based on the relationship obtained, we employed a single equivalent structural strut represented by a nonlinear axial member, acting in the diagonal direction of the frame, in simulating the infilled RC wall to simplify the framed wall model. As a result, the sequential pushover analysis of the whole structure could be performed easily and efficiently based on the realistic procedure proposed. To validate the proposed approach, reported results from the cyclic loading tests of fifteen specimens were adopted for the correlation. Based on the correlation, it is found that this study can provide an acceptable result of the pushover analysis and give an insight into progressive failure consequence of the framed wall structure. The proposed procedure simplifying the structural model helps the practical engineers get a higher efficiency while performing seismic evaluation and retrofit design of the high-redundancy frame structure with numerous infilled low-rise RC 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".