Bibliographic record
Abstract
A previously developed computer model was used to investigate the effects of a wide range of parameters applicable to concrete block masonry infilled steel frames. Height to length panel aspect ratios were varied from 0.5 to 1.5 to reflect how other parameters were affected by these values. Eight different types of parameters were studied. The method of applying horizontal load was found to have little effect. Isolation gaps between panel and beam reduced both the stiffness and strength of the infilled frame. While panel-to-column ties generated an increase in peak load, local stress concentrations caused by the ties resulted in additional deterioration of the panel. Strength was found to vary with mortar joint bond strength, with the effects being more significant at higher aspect ratios. Increasing beam stiffness increased strength for low aspect ratio frames, and increasing column stiffness had a similar effect for high aspect ratio frames. Gravity loading was beneficial in increasing shear resistance up to a limit where it caused crushing of the masonry infill. The increases in strength of infilled frames were found to be disproportionate to increases in the strength of the masonry.Key words: masonry, steel, infill, frame, analytical, variables, shear, strength, deflection, interaction.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".