Use of hygrothermal modeling to validate the application of an open-cell spray-foam insulation application to a high-rise heritage building in Winnipeg, Manitoba
Bibliographic record
Abstract
The increasing cost of new building construction has made repurposing existing building stock economically more viable compared with a green site new build. In addition to capital cost benefits, increasing urban densification through repurposing existing buildings is one of the solutions for enhancing the urban environment. This paper summarizes the investigative work completed to substantially improve the energy efficiency of a heritage 10 storey building in Winnipeg, Manitoba. The investigative work for the remediation involved hygrothermal modeling to rationalize the introduction of thermal insulation to the interior face of the exterior walls. Based on the modelling, an open-cell spray foam was applied to the interior face of the masonry walls. Temperature, moisture, and relative humidity sensors were placed in various locations throughout the building to monitor the exterior walls, primarily to identify if deleterious levels of moisture were accumulating in the masonry. The results were also used to compare the in-situ performance of the building with the predicted performance from the hygrothermal models. The investigation confirmed that obtaining accurate in-situ moisture readings in masonry products is significantly impacted by initial internal moisture levels, necessitating pre-test calibration. Regardless, moisture sensor data accumulated to date indicate that after six years of monitoring, deleterious levels of moisture in the masonry and plaster are not occurring in the exterior walls, which is in good agreement with hygrothermal model results for open-cell foam. The results of this investigation confirm that the implementation of hygrothermal modeling is an effective and accurate analysis tool in the long-term durability assessment of building envelopes for heritage buildings.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".