Thermal resistance of masonry walls: a literature review on influence factors, evaluation, and improvement
Bibliographic record
Abstract
Increasing the thermal resistance of masonry wall systems is one of the effective ways to reduce energy consumption in the operation of masonry buildings. This increase is also demanded by newer, more stringent energy codes. However, the effective thermal resistance ( R-value) of masonry walls is affected by many factors, such as thermal bridging, which occurs in places where highly conductive structural components penetrate insulating materials. Thermal bridging is common when connecting masonry veneers to structural backup walls. Furthermore, quick and precise methods for estimating the R-value are needed for thermal design improvements and code-compliance calculations. This study presents a comprehensive literature review on key factors that influence the overall thermal performance of masonry walls, methods to effectively estimate and measure R-values, and improvements in thermal design. In addition to identifying the main technical and practical challenges and the corresponding progress made on each front, key design considerations, such as code compliance, material properties, insulation types, and location, as well as special ties and shelf angles types, are also discussed. This study summarizes critical information and recommendations that will help improve the thermal design of masonry walls, hence reducing the energy consumption of buildings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".