Thermal resistance of masonry walls: a literature review on influence factors, evaluation, and improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it