Evaluating Uncertainty in Hygrothermal Modelling of Heritage Masonry Buildings
Bibliographic record
Abstract
Hygrothermal models are important tools for assessing durability risks in building envelopes, such as biological growth (mould and wood rot fungi), corrosion and freeze-thaw action in masonry. Hygrothermal modelling of mass masonry wall assemblies are known to have numerous weaknesses and gaps in our understanding. First, there are significant uncertainties relating to model inputs including material properties and boundary conditions. Second, it is difficult to calibrate model results against data measured in the field. Third, two and threedimensional interactions between adjacent materials in masonry assemblies are poorly understood and are rarely modelled in practice. And fourth, geometric irregularities, imperfections and the effects of decay are rarely considered by modellers. Combined, these uncertainties can lead to reduced confidence in the model's conclusion and alter our opinions on the durability risks and whether retrofits such as interior insulation are appropriate or not. This doctoral thesis examines how uncertainty factors into hygrothermal modelling of heritage masonry, and how it can be reduced, and or, acknowledged in practice. This is demonstrated using a combination of simulation studies and on-site monitoring work. Three peer-reviewed papers which demonstrate examples of uncertainty in hygrothermal modelling are presented.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".