High-Performance Brick Mortar Mix to Optimize Moisture Management in Brick Wall
Bibliographic record
Abstract
The durability of exterior building envelopes is significantly impacted by the presence of water, particularly through the capillary rise mechanism that allows liquid water to penetrate building materials. This process affects both the energy efficiency and durability of buildings. To assess the capillary water intake into porous building materials, the water absorption coefficient is used as a characterization parameter. Additionally, the water vapor permeability of a material indicates its ability to allow moisture to diffuse and escape. In this project, two concentrations of zinc stearate (0.5% w/w and 1% w/w) were added to commonly used mortar. Following the ASTM standard test procedure, the liquid water absorption coefficients and water vapor permeability of brick, mortars, and brick mortar joints were determined. These experimental values were utilized as inputs for the hygrothermal performance analysis (numerical modelling) of the brick wall assembly. The experimental findings suggest that the addition of zinc stearate to the mortar can reduce water absorption capacity while simultaneously enhancing water vapor permeability. Numerical modelling results further demonstrate that the use of high-performance brick mortar materials can significantly improve the moisture management capability for brick walls in the marine-warm and humid climate of Vancouver, BC, Canada.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".