Modelling and analysis of retrofitted and un-retrofitted masonry-infilled RC frames under in-plane lateral loading
Bibliographic record
Abstract
In the context of various developing countries where many old structures require retrofitting or strengthening work to mitigate earthquake hazards, a cost-effective method is the retrofitting of damaged masonry-infilled reinforced concrete (RC) frames using ferrocement overlays, and the strengthening of existing infilled RC frames with ferrocement. However, no reliable mathematical or computational tool is accessible in the open literature to estimate the effect of such a retrofitting technique quantitatively. The present study is a numerical investigation of the retrofitting effect of masonry-infilled RC frames using ferrocement. A finite element technique has been used effectively to develop a computational model for analysing bare RC frames, together with un-retrofitted and retrofitted masonry-infilled RC frames. The proposed model accounts for the material nonlinearities of both concrete and masonry, and the yielding of reinforcing steel. It is shown that the proposed model can be used effectively in predicting the load carrying capacity of existing RC frames, as well as the required degree of strengthening when ferrocement overlays are applied as a retrofitting scheme. A parametric study was performed using the proposed model on bare and infilled frames to quantify the effects of different parameters. This enabled the development of a simplified equation for predicting the ultimate load carrying capacity of masonry-infilled RC frames, which proved to be reasonably accurate and which was validated by both experimental and numerical results.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".