Sensitivity of Hygrothermal Analysis to Uncertainty in Rain Data
Bibliographic record
Abstract
Abstract This paper describes a small study carried out using a hygrothermal simulation tool to investigate the sensitivity of wall performance results to uncertainty in the amount of rain impinging on the wall. Design standards for hygrothermal analysis of proposed designs include methods for selecting appropriate moisture reference years and specify the amount of water that is assumed to intrude into the wall. Weather data used as input for modeling purposes is generally assumed to be reliable, but recent work has shown that there may be considerable uncertainty in the rainfall data. A small study was carried out to investigate the effect of uncertainty of rainfall data on the hygrothermal performance of a typical residential building envelope. Most hygrothermal models require fully populated hourly datasets, which include rain data. Many locations, however, do not have this kind of data although many have qualitative rain data. Ten locations with rain gage data were chosen as typical of most regions of Canada, except for the far north. Different methods in estimating rainfall were considered as well as variations on the amount of rain data were subsequently made. Several performance criteria, including total moisture content and a mold index, were compared. Although the choice of which method for deriving quantities of water from qualitative codes does cause differences in the hygrothermal response and consequently the performance criteria, these differences appear to be manageable. It is suggested that practitioners should show their awareness of the probable level of uncertainty by stating error bands for their predictions of performance. It should be emphasized that the sources of uncertainty dealt with in this small study are not the only ones, but that they do focus on water entry through leakage paths. The natures of the leakage paths likely introduce greater uncertainty, and should also be borne in mind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".