Controlling Thermal Bridging as a Value-Added Technique to Enhance Energy Efficient Building Envelopes
Bibliographic record
Abstract
Buildings consume 30% to 40% of all primary energy worldwide and are responsible for 50% of total greenhouse gas emissions. Canada has set a target of reducing greenhouse gas emissions by at least 80% by 2050. The aim of this paper is to investigate thermal bridging in four high-efficient buildings, such as those built to LEED standards. Thermal bridging is a major source of heat loss in many buildings, manifesting itself through exterior envelopes, particularly through studs and wall systems. LEED is an evaluation system that rates how sensitive buildings are to the environment, with one of its main objectives being the reduction of greenhouse gases (GHG) emissions through the implementation of highly efficient mechanical systems and the design of durable and efficient exterior wall systems with appropriate insulation. This research investigates and identifies the location of thermal bridging in high-efficiency buildings using nondestructive testing methods such as thermal imaging and THERM simulations. The study involves using infrared thermography to inspect surface temperature variations and detect irregular thermal patterns that correspond to thermal bridging. By collecting record drawings to identify the construction systems used in the external wall compounds, capturing thermal images with high-resolution infrared cameras, and comparing these images with simulation results, the research provides a comprehensive analysis of thermal performance. The findings from this research are significant, particularly in the context of window curtain walls, steel studs, brick shelf angles, timber frames, and roof hatches—all of which were identified as critical areas of concern due to their propensity to thermal bridges. For instance, window curtain walls, with their metal frames and large glass surfaces, showed temperature drops of up to 8°C. Steel studs, which are highly conductive of heat, resulted in temperature drops of up to 5°C, while brick shelf angles showed a temperature drop of 4°C due to heat conduction through the metal. In timber frame structure the building envelope connector showed temperature drops of up to 7°C. Additionally, roof hatches, necessary for rooftop access, were found to cause the most significant temperature drop of 10°C. The findings highlight the significant impact of thermal bridges on overall energy efficiency, emphasizing the need for careful design and construction practices to minimize heat losses. Addressing these thermal bridging issues is crucial for achieving the ambitious GHG reduction targets and enhancing the sustainability of high-performance 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".