Strengthening of a concrete masonry wall subject to lateral load with sprayed glass-fibre-reinforced polymer
Bibliographic record
Abstract
Unreinforced hollow concrete masonry walls could be used to construct basements if strengthened to resist the lateral load. Two face-shell-bedded concrete masonry walls were constructed, 3 m high by 6 m long. As a simple strengthening technique, one wall was sprayed with glass-fibre-reinforced polymer (GFRP) on one side to a nominal average thickness of 5 mm. The walls were subjected to distributed point loading simulating increasing pressure from top to bottom of the wall. Support conditions were applied to simulate the walls being part of a basement. The plain wall failed with a failure line cracking pattern at a lateral load of 44 kN. The sprayed wall failed in a much more brittle fashion when the load reached 330 kN. The GFRP suffered a mode III tear at the bottom course. The results indicate that spraying a plain masonry wall with GFRP increases its ability to resist lateral load considerably, and that the process could be improved in terms of both the thickness of the layer and the area of wall covered to achieve a specific target. The two walls were analyzed using the yield-line, fracture-line, and failure-line methods. The failure-line method was improved by accounting for the stiffness orthotropy of masonry and gave the most accurate prediction of these plastic design methods. A finite element model of the masonry provided the most accurate prediction of capacity.
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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) | 0.001 | 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".