Metamodel Development for Predicting Hygrothermal Performance of Wood-Frame Wall under Rain Leakage
Bibliographic record
Abstract
In recent years, stochastic modeling has been increasingly applied to investigate the uncertainties of input parameters in hygrothermal simulation and the moisture damage risks of building envelopes. Generally, stochastic modeling requires hundreds or even thousands of simulations to take into account the uncertainties of input parameters, which is computationally intensive and timeconsuming. This paper aims to apply polynomial and neural network metamodel as a substitute for the traditional hygrothermal model, to predict the hygrothermal performance of building envelopes. In the previous study carried out by the authors, stochastic simulations have been performed based on the traditional hygrothermal model, to investigate the hygrothermal performance of wood-frame walls under different rain leakage levels. The material properties and rain deposition factors were considered as stochastic variables, and stochastic simulations were performed under three rain leakage scenarios: 1%, 0.5% and 0.1% of wind-driven rain. In this paper, the stochastic inputs (the hygric material properties and rain deposition factor) and outputs (the maximum moisture content and mold growth index over a 5-year period of the simulation) of a conventional 2×6 wood-frame wall are used to develop the metamodels through polynomial regression and neural network methods. The metamodels are developed for each rain leakage scenario, and the stochastic data of the three rain leakage scenarios are aggregated together to train another metamodel. It is found that the metamodels generally perform well to predict the maximum moisture content and mold growth index. The metamodels for low rain leakage scenarios are better than those for high rain leakage scenarios and the neural network metamodel is more accurate than polynomial metamodel for high rain leakage scenarios, i.e. 1% of rain leakage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".