Adjustments of existing analytical methods for estimating the effective thermal resistance of clear masonry cavity walls
Bibliographic record
Abstract
: Growing demands for energy efficiency in buildings have driven the improvement of thermal resistance (R-values) in building envelope components. However, highly conductive components, such as thermal bridges, can significantly reduce the R-value. Evaluating the R-values of the building envelope is essential for a reliable assessment of buildings' thermal behaviour and energy efficiency. The lateral heat flow in multiple directions, caused by thermal bridging, remains a challenge for accurate R-value estimation. Current analytical methods lack the precision to account for multidirectional heat transfer through masonry walls due to their geometric complexity and the presence of conductive components that penetrate one or more layers. To address this limitation, this study introduces adjustment factors that enhance existing analytical R-value estimation methods, specifically the isothermal plane and parallel path methods, to account for the effects of thermal bridging in clear masonry cavity walls. The adjusted analytical R-values were validated against numerical simulations using a three-dimensional finite element method and experimental data available in the literature. The results showed an average error of 2% in using the suggested adjustments, compared to 19% and 25% for the isothermal plane and parallel path methods without adjustments, respectively. The findings demonstrate that the developed adjustment factors significantly improve the accuracy and applicability of analytical R-value estimation for masonry cavity walls. The proposed approach offers a practical and reliable method for assessing the thermal performance of masonry envelopes, thereby contributing to more energy-efficient and sustainable building design.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".