Performance enhancement of RC structures through concrete jacketing: A structural rehabilitation approach
Bibliographic record
Abstract
Concrete buildings are facing some serious issues around the world. There are a few reasons for this, natural disasters such as earthquakes, lack of knowledge about important building codes, and poor supervision during construction. Because of these problems, many buildings are weaker than they should be. If these structures are under too much weight, they can bend and corrode, which means immediate repairs are needed. To tackle these problems with reinforced concrete, repair and strengthening methods have become really important in construction today. Even new buildings sometimes end up needing fixes because of design mistakes or problems during building. Structures that have been damaged by unexpected events like fires or earthquakes need special techniques to make them strong again. Fixing up buildings helps protect them from earthquakes and reduces the risk of damage. It's all about boosting a building's strength to meet safety standards. Many studies have looked into effective ways to reinforce them. This paper will take a brief look at some new and cost-effective methods for repairing damaged buildings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".