Methodologies for evaluating exposure and response of stone masonry to wind-driven rain
Bibliographic record
Abstract
Wind-driven rain (WDR) is a main moisture source and weathering factor for monumental and vernacular stone masonry in the UK. To conserve and manage these structures, especially as weather events are predicted to become more intense during the 21st century, methodologies are needed that: (a) characterise environmental WDR exposure, and (b) non-destructively monitor the response of moisture regimes within stone masonry. This thesis aims to address exposure and response between WDR and stone masonry, integrating characterisation and methodological development with an emphasis on data handling and visualisation. Semi-empirical approaches are employed to characterise current and future WDR exposure in the UK to evaluate existing standards and metrics. The use of non-destructive electromagnetic techniques for moisture measurement is explored for comparative advantages when applied for stone masonry. Extreme value analysis (EVA) is used to evaluate severe WDR exposure in the UK at eight sites. While reinforcing established trends (e.g. prevailing wind directions) this research highlighted the impact of wall orientation on the volume of water and consistency within WDR spells and their quantity and duration. The EVA demonstrated that current standards (ISO 15927-3 and BS 8104) underestimate extreme exposure. A combination of UKCP09 Weather Generator output with probabilistic processes demonstrated that existing contrasts between sites will be magnified by predicted climatic changes and become more seasonally polarised, providing an impetus to improve current standards by incorporating extreme value analysis and temporal metrics. A novel, cost- and time-effective method of laboratory gravimetric calibration using 'isolated diffusion' was validated, which produced calibrations of radar and microwave techniques for three UK building stones that matched modelled behaviour. The combined use of microwave and radar techniques in field studies on two stone masonry constructions characterised localised moisture regimes within stone masonry systems (stone units and mortar joints), demonstrating that technique selection is optimised with consideration for material properties and the investigation objective. Innovative data handling and visualisation strategies demonstrated their utility for these scenarios of stone masonry composed of different materials. By developing methodologies for semi-empirical evaluation and non-destructive techniques, as well as characterising environmental and hygric properties/behaviour of stones and stone masonry, this thesis has contributed to both progress in scientific research and practical aspects of heritage conservation in the context of a changing 21st century climate.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".