A Methodological Approach to Predict and Estimate Air Leakage Through a Window-To-Wall Interface in Wood Frame Construction
Bibliographic record
Abstract
Airtightness is an important phenomenon in building performance. Poor building airtightness can impact the IAQ, indoor comfort conditions, building envelope’s structural integrity, occupant’s health, and building energy performance. It is well documented that window-to-wall interfaces are an area of reoccurring air leakage location, contributing 15% to 35% to whole building air leakage. Therefore, there is a demand for adequate prediction methods to estimate air leakage through the window-to-wall joint to assist builders and designers in making cost-effective design decisions prior to construction. The objective of this study was to further an approach to model preconstruction relative airtightness of differing window-to-wall joint detail designs and variation in field construction. This research was done to further the work already conducted in determining if an experimentally based methodology can be used to alter predictions of whole building airtightness in new construction at the pre-construction phases of development (Khemet & Richman, 2020). An empirically-based design of experiments was created using a 23x3 factorial design to quantify the impact of air leakage through the window-to-wall joint. The four main effects that were selected for analysis were pressure, shim type, sealant technique, and detail length. The multiple linear regression model explained up to 38.7% of the air leakage through the joint (R=0.6387, p<0.001). The results of the experiment established an approach to predicting relative air leakage of varying window-to-wall joints.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".