Carbonation of Dolomitic Type S Lime-Based Masonry Mortars
Bibliographic record
Abstract
Quicklime, created by burning limestone, reacts with water to form a fine powder called hydrated lime, which in turn absorbs carbon dioxide from the air to return to its original form, limestone. In masonry applications, the carbonation of hydrated lime used in mortars can strengthen the mortar over time and help heal fine cracks by a mechanism called autogenous healing. Though data exists regarding carbonation of high-calcium hydrated lime in mortars, there have not been studies regarding carbonation of dolomitic hydrated lime in this application. There is also only limited data on the effect of portland cement on hydrated lime carbonation. This study addresses these two areas by exploring the carbonation of dolomitic Type S hydrated lime in three different mortar types (Lime-Sand, Type O, and Type N). Cubes and prisms containing these mortar types were exposed to laboratory and field conditions for up to 720 days. Samples of each mortar were analyzed at four different ages to determine the extent of carbonation. Analysis methods included compressive strength, change in weight, depth of carbonation using phenolphthalein pH indicator, and thermal analysis. The study indicated that significant carbonation occurred over the two year period of testing but determining a precise measure of its extent was not possible, due in part to the dolomitic lime chemistry and in part to the presence of portland cement. Compressive strength and change in weight did not provide a clear indication of the extent of carbonation with time for mortars containing portland cement. Phenolphthalein testing was misleading, apparently because it was influenced by the low pH of magnesium hydroxide. Given the limitations of the methods used in this study, further work is needed to develop better techniques for mortar carbonation measurement in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 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 teacher head, 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".