A probabilistic approach for risk assessment of moisture-related degradation of building envelopes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The performance and durability of wood-frame building envelopes is affected by long-term moisture transport and its impact. Despite considerable progress in deterministic and prescriptive methodologies aimed at estimating moisture deposition and the consequent risk of mold growth, a consensus in methodology applicable to the analysis of moisture risk in building enclosures is an unfinished agenda. This might partly be caused by uncertainties that exist due to variations in input parameters, model structure, and data scarcity. To address this issue, this study presents a probabilistic risk assessment of building envelope deterioration from moisture accumulation. The proposed methodology integrates the development of meta-models, a full-factorial response surface methodology, and Bayesian analysis. The effectiveness of the proposed approach is demonstrated through a parametric analysis of typical wall assemblies featuring diverse layers and boundary conditions. The findings highlight the influence of input variables and their relative significance on moisture accumulation in the selected climate zones. Additionally, a sensitivity analysis of model parameters and the application of Bayesian analysis in specific contexts are presented, facilitating comparative evaluation of moisture-related risk of building envelopes.
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.
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) | 0.000 | 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 it