Two-dimensional hygrothermal modelling of masonry walls accounting for imperfections at the masonry joint
Bibliographic record
Abstract
Hygrothermal models are important tool for assessing the risk of moisture-related decay mechanisms such as freeze-thaw in historic masonry structures. There are several sources of uncertainty when modelling masonry, related to material properties, boundary conditions, quality of construction and twodimensional interactions between mortar and unit. This paper examines one potential source of uncertainty; the imperfect nature of mortar joints. This interface may feature hairline cracks or imperfect bonds which can be modelled as a fracture. This will alter the rate of liquid transport into and out of the wall and impede the liquid transport between mortar and masonry unit. This means that the “effective” liquid transport of the wall system will be different then if measured properties of the bulk material were modelled. A detailed methodology for modelling the interface as a fracture is presented including material property definition. Two-dimensional DELPHIN models of masonry walls were created to simulate this interaction with varying levels of fracture widths (apertures). A series of hygrothermal simulations were performed to demonstrate change in moisture profile from the baseline condition. A significant increase in moisture absorption was found. This was dependent on aperture size, material and the relative size of the masonry modelled.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".