Assessment and Retrofitting Of a Multi-Storey Reinforced Concrete Building
Bibliographic record
Abstract
Due to safety issues, many of the existing reinforced concrete structures in Cyprus, built 50 years ago, require immediate retrofitting.In general, a great number of structures have issues with reinforcement corrosion and damaged concrete, and in some instances, they are unsafe even under dead loads.Considering the high seismicity of the area, the retrofitting of buildings is crucial.This research work presents the seismic assessment of an existing multi-storey reinforced concrete building, considered to have been built with the prevailing construction practices in Cyprus in the 1970s.Furthermore, it examines various retrofitting strategies that meet the seismic requirements of the region where it is located.Specifically, strengthening with reinforced concrete walls, and concrete jacketing of various layouts and combinations are investigated to compare their contribution towards the seismic upgrade of the structure.Both the assessment and the retrofitting are conducted using non-linear static (pushover) analysis.The results show that the optimal seismic upgrade of the structure in terms of structural response to anticipated earthquakes in the area is achieved by integrating infill walls and concrete jacketing strategically in critical locations.Summing up, the present work investigates the seismic behaviour of a reinforced concrete building before and after strengthened with different combinations of concrete jacketing and reinforced concrete 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".