A Rapid Review of Hygrothermal Performance Metrics for Innovative Materials in Building Envelope Retrofits
Bibliographic record
Abstract
With government, industry, and public pressure to decarbonize the building sector through reducing embodied and operational emissions, there have been a wide range of innovative materials used in building envelope retrofits. Although these innovative materials, such as super insulating materials, bio-based insulation, and many others, are assessed on thermal performance and code requirements before use in retrofits, there is no unified standard assessment metric for hygrothermal performance of innovative materials in building envelope retrofits. This paper performs a rapid review of the available literature from January 2013 to March 2025 on hygrothermal performance assessment metrics used in retrofits. Using rapid review methods to search for records in Scopus, Web of Science, and Google Scholar, fifty-nine publications were selected for bibliometric and qualitative analysis. Most selected publications include discussions and analysis of relative humidity in the wall assembly post retrofit, moisture content, and mould index within the envelope. There is a research gap in publications considering hygrothermal damage functions such as freeze–thaw index, relative humidity and temperature (RHT) index, or condensation prediction. There is also a research gap in country and climate studies and analyses of in situ retrofits with innovative materials, and occupant comfort post retrofit.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.039 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".