Developing a calibrated in-situ hygrothermal model of a community center to assess panelized exterior retrofit design suitability.
Bibliographic record
Abstract
Over 70% of buildings in Canada were constructed prior to the implementation of the Ontario Building Code of 1975, the first code in Canada to include energy requirements. Many of these buildings are reaching the end of their useful life and require building envelope repairs where the alternative is to demolish and rebuild, which is less sustainable. Performing a retrofit on the building envelope can improve the buildings air tightness and thermal resistance. While these improvements increase the energy efficiency of the building, it also creates potential for the building envelope to accumulate moisture if the design is not carefully selected.This paper describes the development of a calibrated hygrothermal model utilizing in-situ data. The model can then be used to assess the performance and suitability of two different retrofit panel designs for a community centre, which has a corrugated steel siding envelope, located in Ile Bizard, an island in Montreal, Quebec, Canada. In-situ data from two, 3 m by 3 m test walls, with sensors installed throughout, is utilized to calibrate the hygrothermal WUFI model leveraging differential evolution methods. To minimize the residual error between the measured and simulated data, an objective function utilizing the Huber loss function was generated for the relative humidity in the layers of each wall assembly. The calibration process adjusts the air exchange rates, locations, and direction within the assembly. This calibration process was shown to improve agreement for relative humidity with the measured data by 30% when compared to the average simulation results from five different engineers. The calibration process was successful and through the calibrated models it was determined neither retrofit panel poses a mould risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".