Combined Use of Wind-Driven Rain Load and Potential Evaporation to Evaluate Moisture Damage Risk: Case Study on the Parliament Buildings in Ottawa, Canada
Bibliographic record
Abstract
Parts of the building envelope that frequently receive high amounts of rain are usually exposed to a higher risk of deterioration due to moisture. Determination of such locations can thus help with the assessment of moisture-induced damage risks. This study performs computational fluid dynamics (CFD) simulations of wind-driven rain (WDR) on the Parliament buildings in Ottawa, Canada. Long-term time-varying wetting load due to WDR and potential evaporation are considered according to several years of meteorological data, and this cumulative assessment is proposed as a fast method to identify critical locations and periods. The results show that, on the Center Block of the Parliament buildings, the façades of lower towers facing east are the most exposed to WDR, together with the corners of the main tower. Periods of high WDR wetting load larger than the potential evaporation are observed, indicating that deposited rain may lead to moisture accumulation in the envelope. During these critical periods of up to several months, air temperature may repeatedly drop below freezing point, which poses a risk of freeze–thaw damage. First assessment on future freeze–thaw damage risks indicates an increase in such risks at moderate increases in temperature, but a lower risk is found for larger increases in temperature.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".