An Investigation of Climate Loads on Building Façades for Selected Locations in the United States
Bibliographic record
Abstract
The ability of a wall assembly to manage rainwater and control rain penetration depends on the assembly configuration, including interface details for penetrations, and on the rain loads to which the wall is subjected. There are a variety different protocols for evaluating the ability wall systems to resist water intrusion. Generally they involve spraying varying amounts of water while maintaining a pressure difference across the specimen. Across the conterminous United States hourly weather data for extended periods (climatic data) is available for many locations. From this climatic data estimates of wind-driven rain loads can be determined. We answer the question of how often these combinations of rainfall intensities and pressure are likely to present a problem with respect to moisture management of the assembly and how often these are likely to occur over the expected life of the wall assembly. Climate information related to rainfall and wind-driven rain for Boston, Miami, Minneapolis, Philadelphia, and Seattle are provided. A methodology for generating rates of water spray impinging on and pressure differences acting across the wall assembly is also developed. Although the methodology was primarily developed to select the proper testing criteria and test conditions to mimic real events, it can also be used by designers and practitioners to: (i) determine the response of the wall assembly to the effects of wind-driven rain; (ii) estimate design loads below which adverse effects on the assembly are minimized; (iii) assess the likelihood and degree of damage to the assembly when design loads are exceeded; (iv) estimate the long-term performance of the wall assembly based on watertightness and moisture management the wall assembly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".