Behavior of Externally Reinforced Post-Tensioned Shear Walls under Lateral Loads
Bibliographic record
Abstract
In high seismic regions, numerous older reinforced concrete (RC) buildings, constructed before modern seismic codes require strengthen to withstand major earthquakes.This research presents an experimental and numerical investigation of strengthen technique for shear walls that are susceptible to brittle failure modes due to deficient detailing or a lack of properly confined boundary elements.The strengthen process involves the addition of steel members.This study primarily focuses on comprehending the flexural characteristics and lateral resilience of a scaled specimen: post-tensioned (PT) walls.Following testing until failure, a strengthening strategy was implemented by attaching steel components to the lower section in the PT wall.These modified configurations are now recognized as the other specimen.Importantly, the ABAQUS model effectively simulates the lateral response of the tested specimens, with minor variations compared to the experimental results falling within an acceptable range.The strengthened walls exhibited ductile failure, as opposed to the original walls that experienced brittle failure.Furthermore, the incorporation of steel components had minimized cracking, increased load-bearing capacity, and improved stiffness and ductility.This enhancement is anticipated to minimize earthquake-induced damage, leading to shorter repair times.
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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 | 0.001 |
| 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 | 0.001 |
| 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".