Applicability of ASHRAE’s damage function to predict moisture severity of climate for Canadian locations
Bibliographic record
Abstract
Hygrothermal simulation tools are commonly used to assess the moisture performance of building envelope components. Owing to the computational costs required to complete simulations over the long-term, one approach to reduce simulation time when undertaking hygrothermal design analysis is to select representative year(s) amongst sets of long-term climate data. To properly select these moisture reference year(s), a method is required to predict moisture performance and rank the climate years in terms of their moisture severity. To this end, several methods have been proposed in the literature, amongst which is the damage function method as reported in ASHRAE Project Report RP-1325. In this method, a stepwise regression model was developed to predict the damage function, as characterized by the RHT-index (integral of (Temperature - 0) (Relative Humidity – 70%)), in an OSB layer of a wood-framed wall as a function of several average yearly climate parameters for a North facing wall. The model was calibrated using climate and simulation data for eight cities in the USA and validated for three cities in the USA and one city in Canada (Winnipeg, MB). The method was found to be the most consistent and accurate amongst all ranking methods that have been evaluated. The objective of this paper was to: (1) evaluate the ASHRAE’s method for several Canadian locations; (2) determine whether the original model can be recalibrated in the event it is shown to be deficient; and (3) explore the potential of improving the model using other approaches such as the Partial Least Squares Regression (PLSR), the Least Absolute Shrinkage and Selection Operator (LASSO) regression, and the combination of LASSO feature selection and Support Vector Regression (SVR). The results suggest that for some Canadian locations, as were investigated in this study, the use of the original model may not be appropriate for predicting the moisture performance and ranking of climate years in terms of their moisture severity. However, the model was shown to perform better after it was recalibrated for Canadian locations but without improvement in ranking performance. Furthermore, the damage function model can be improved by using either PLSR, LASSO or SVR in terms of prediction and ranking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".