Examining the impact of stochastic multi-year weather and air infiltration on hygrothermal moisture risks
Bibliographic record
Abstract
The analysis of heat, air, and moisture (H.A.M.) transport for building envelopes are known to be highly dependent on climate loads and air infiltration rates. Moisture content within the assembly is often a key H.A.M. analysis outcome to assess risk and transport behavior. ASHRAE Standard 160-2016 states that building envelope H.A.M. analysis should be done using moisture design reference data or using a minimum of 10 consecutive years of weather. While there has been progress and methods for selecting or designing moisture reference years there has been a lack of study in the impact of multi-year (particularly 10-year) weather scenarios on simulation results in comparison to reference year simulations. This paper presents research using stochastic 1, 2, and 10-year weather data and air infiltration rates to study the range of simulated moisture content outcomes for four wall assemblies in Philadelphia and compares these to the outcomes when using reference years. Results from the study show that air infiltration, starting month, and multi-year duration have significant impacts on simulated moisture content, mold, and corrosion analysis results. Regression analysis using annual averages of climate input parameters did not yield useable models for selecting weather years, however an estimated mold index value using outdoor climate data may be useful in selecting weather years with varying starting months for mold growth assessment.
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 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".