Impact of Stationary and Dynamic Conditions on the U-Value Measurements of Heavy-Multi Leaf Walls by Quantitative IRT
Bibliographic record
Abstract
Infrared thermography (IRT) has become a commonly applied non-destructive testing method for assessing building envelopes. Like any diagnosis tool, IRT requires an appropriate experience and principle understanding, mainly when the method is used for quantitative analyses. The challenges of the IRT often deal with the dynamic properties of building partitions. Climatic conditions have a certain variability, and the accumulated energy storage in the building components can affect their temperature as well as the calculated thermal performance. This paper aims to analyze how stationary and dynamic regimes of a quantitative IRT test could impact the measured thermal transmittance of heavy multi-leaf walls. Investigation in two European countries with different climatic conditions are reported. In this way, it is discussed which boundary conditions should be guaranteed to provide reliable information about a building envelope using quantitative IRT. In order to check the quality of the measurements, the heat flux meter (HFM) method was also implemented, following the ISO 9869. The research revealed that it could be possible to use short-lasting tests in the climatic conditions of Southern Europe, while long-term tests should be implemented in Northern European countries where climatic conditions are less regular.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".