Seismic Retrofit and Strengthening of Deficient Reinforced Concrete Shear Walls Using Externally Bonded Fibre Reinforced Polymer Sheets
Bibliographic record
Abstract
Seismically deficient reinforced concrete structures are widely present in many regions of the world.Structural deficiencies commonly found in old reinforced concrete structures are poor confinement, poor detailing and the presence of non ductile details (lap splices) at the plastic hinge region.The adverse effects of these deficiencies often translate to poor seismic performance due to poor energy dissipation capabilities, poor ductility behaviour, and the association of these effects with brittle failure mechanisms.The Seismic response of old deficient shear wall structures built in the 1960's and 1970's is investigated.The wall specimens investigated contain poor confinement, insufficient shear reinforcement, and lap splices of the longitudinal reinforcement located at the plastic hinge region.As a result of these deficiencies, the wall specimens have non-ductile response behaviour and require retrofit to enhance their seismic performance.Nine shear wall specimens with aspect ratio ranging from 0.65 to 1.20 subjected to quasi-static reverse cyclic loading are investigated as part of a comprehensive research program.Analytical simulations utilizing the finite element method are conducted to predict the response of the wall specimens.The use of carbon fibre reinforced polymers (CFRP) tow sheets in both the transverse and longitudinal directions to mitigate the structural deficiencies of the wall specimens is evaluated.Analytical results show that the numerical models can accurately predict the elastic and inelastic behaviour, peak load, maximum displacement, energy dissipation and the failure mechanisms of shear walls strengthened and repaired with FRP tow sheets.The fibre reinforced polymer material is effective in eliminating the brittle shear failure mode in walls with insufficient shear reinforcement.
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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 | 0.000 | 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) | 0.002 | 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".