Development of climate-based indices for assessing the hygrothermal performance of wood frame walls under historical and future climates
Bibliographic record
Abstract
It is generally understood that the average temperature of the earth is increasing, resulting in an increased number of extreme events. To assess the impact of climate change on the durability of the building envelope, a commonly used method is to use hygrothermal modeling tools to perform simulations. The hygrothermal response varies depending on the location, material properties, type of wall assemblies, etc., and hence proper inputs are required. In general, indicator based on simulation results indicates the moisture risk. However, to obtain this indicator and considering different situations, a large number of simulations are required. This research thus focused on developing a climate-based index that can give a range of expected performance of the wall without performing the simulations. \nFirstly, different existing climate-based indices were computed and correlated with the performance indicator to quantify the risk. The purpose is to see if any existing climate-based indices can lead to accurate risk assessment and the analysis showed that none of these indices lead to reliable risk assessment. Thus, a machine learning algorithm, Partial Least Squares (PLS) regression was used to develop a new climate-based index. Three cities from different climate zones across Canada and two wall claddings were considered for model development. For each city and future projected climate, the index was calculated, and correlated with the performance indicator to quantify the risk. PLS modeling technique proves to be an effective way in predicting the hygrothermal response and to improve computational efficiency. A PLS model was developed for a brick cladding wall and the model was applied to other wall types and a larger climate range (15 runs of data with each run having 31 years of historical and future climate data). The results showed that the moisture risk increases in the future periods for all three cities and wall claddings and a similar performance was noted for different climate runs. The predicted results from the meta-model can be used as a screening measure to limit the number of simulations to cases where the predicted hygrothermal performance is above a certain threshold set by the user.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".