Representative meteorological data for long-term wind-driven rain obtained from Latin Hypercube Sampling – Application to impact analysis of climate change
Bibliographic record
Abstract
Accurate estimation of wind-driven rain (WDR) load on building facades is of paramount importance for the assessment of moisture-induced damage risks. The response of the facade depends on the used meteorological data, which can show significant variation over time, especially considering climate change . In this study, a statistical approach based on a Latin Hypercube Sampling (LHS) is used to generate reduced samples, which accurately represent long-term meteorological conditions for WDR. Based on cumulative distribution functions, the generated samples with LHS are a subset of actual measured data, independent of the temporal information, and are clustered around values of highest frequency. Computational fluid dynamics (CFD) simulations of WDR are performed on a historical building located in Victoria, BC, Canada based on a previously validated methodology, determining the parts of facade receiving the highest WDR load. The sensitivity study shows that a sample size of 200 with LHS, corresponding to around 0.2% of the total measured data and 4.1% of the data during rainfall, is sufficient to replicate successfully the spatial distribution of WDR with a maximum discrepancy of 7%. The reduced samples can be easily modified to model various scenarios with respect to the climate change. The change in WDR load is presented for different scenarios in terms of rainfall intensity and wind speed as predicted by future climatic conditions . The results indicate that the future WDR load depends highly on the wind speed conditions, even when wind speed is kept constant and only rainfall intensity is varied.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".