Strengthening masonry infill walls with reinforced plaster
Bibliographic record
Abstract
One technique for strengthening masonry infill walls against seismic lateral forces permitted by the Turkish Seismic Code is to add a plaster layer with mesh reinforcement to one face of the wall. The study described in this paper was performed to test and analyse the effectiveness of this technique for lateral loads. In the experimental study five specimens were constructed and tested under cyclic loads. Although the inclusion of the mesh-reinforced plaster layer increased the lateral strength and stiffness of the test specimen 44 and 127%, respectively, one specimen failed prematurely because of insufficient bonding of the dowel reinforcements that were used for transfer shear loads from the frame to the plaster. Direct anchoring of the mesh reinforcement to the reinforced concrete frame instead of using dowel reinforcements in the plaster layer provided a significant improvement in the lateral strength and behaviour of the test specimens. Two alternative layers of materials such as high-strength plaster and shotcrete for improving the strength were used instead of plaster. The most successful lateral performance was obtained from the specimen in which a mesh-reinforced high-strength plaster layer was used. The lateral load-carrying capacity and stiffness of this specimen were increased up to 150 and 250%, respectively.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".