Evaluation of Water Permeability in Fibre Reinforced Hydraulic Lime Mortar Intended for Conservation
Bibliographic record
Abstract
Much of the existing water infrastructure across the world was constructed using masonry in the last 200 years and many of these structures were built with pre-Portland cement binders. Although these mortars exhibit good workability and high water retention in the plastic state, the water tightness deteriorates over the years resulting in a pressing need for suitable repair materials. The addition of polypropylene micorfibre in cement-based systems was found to be effective in reducing water permeability. But the effect of polymeric fibres on the permeability coefficient of hydraulic lime mortar (HLM) is unknown. Therefore, this paper focuses on measuring water permeability in fibre reinforced HLM. Besides, this study examined the application of nanolime onto the aforementioned mortars and its effect on their water permeability. Accordingly, a permeability cell was setup to monitor the onset of the steady state condition in fluid flow. Companion data was generated for the mechanical performance of these mortars. The results show that in hydraulic lime mortar, there is likely an optimal fibre dosage in order to reduce the permeability coefficient. Unlike with Portland cement mortar, this dosage is significantly lower. As well, applying nanolime was most beneficial in limiting water permeability in the natural hydraulic lime mortars.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".