Sustainable Brick Masonry Bond Design and Analysis: An Application of a Decision-Making Technique
Bibliographic record
Abstract
This research intends to explore the sustainable masonry bond formation and interface behaviour of brick masonry bonds with different cement mortar ratios. To test the sustainable behaviour of different brick bonds, different tests were applied to evaluate the performance of the developed five brick masonry structures with the help of four mortar ratios. Following that pattern, the methodologies of a prism triplet test, a bond wrench test, a shear bond test and strength tests for brick masonry were applied. The prism triplet test explained the bonding behaviour of mortar by producing a maximum strength (0.21 MPa) with a 1:3 mix ratio, and the minimum strength (0.095 MPa) with a 1:8 mix ratio. The bond wrench test showed a bond strength of maximum 0.0685 MPa with a mortar ratio of 1:3 and a minimum of 0.035 MPa with a mortar ratio of 1:8. The strength tests for masonry structures expressed that compressive strength (0.786 MPa) and flexural strength (0.352 MPa) were found to be at maximum level with a mortar ratio (1:3) with an English bond formation. For predictions of compressive and flexural strength, artificial neural networks (ANNs) were deployed, and successful predictions of these values along with the relationships between different properties of the material, mortar combinations and bond combinations are presented to complete the exploration of the relationship. This pattern can be helpful for the selection of sustainable brick masonry formations for housing development.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".