Developing New and Optimizing Current Test Methods to Evaluate Sulphide-bearing Aggregate
Bibliographic record
Abstract
This research focuses on testing the applicability on aggregates from Ontario of a testing protocol to evaluate the potential oxidation of sulphide-bearing aggregates, optimizing the protocol test methods, and develop new tests based on the obtained results. The protocol comprises of three sequential tests: Total sulphur content, Oxygen Consumption Test and Oxidation Mortar Bar Test. For the oxygen consumption test, using processing equipment with cast iron media, was found to contaminate the samples and produce high consumption. Moreover, controlling the tested aggregate gradation produces higher consumption and more consistent results than using a fine aggregate sample (<150µm) without controlling the minimum size. The oxidation consumption test showed lower expansion values for carbonate aggregates compared to aggregates with high silicate content. Exposing siliceous aggregate, whether alkali-silica reactive or not, to the high pH from the oxidizing solution and the high temperature produces high expansion regardless of the oxidizable sulphide content. As such, new expansion criteria are suggested which takes into consideration the silicate content of the aggregates. The dissertation proposes, based on testing twenty-six aggregates, modifications to: the oxygen consumption test; the expansion criteria of the mortar bar test; and proposes a new oxidation mortar bar test to avoid the limitation of the applicability of the original mortar bar test on siliceous aggregates. In the new testing program, different testing regimes were investigated using different oxidizing agents and environmental conditions that can promote oxidation of sulphide-bearing aggregates, without the promotion of other chemical reactions (Alkali silica reaction products or Friedel’s’ salt). Out of the various investigated tested regimes, two testing procedures showed promising results and recommended for further development and use. Bleach and lime water are used in these tests as the oxidizing agents; however, mortar bars in both regimes are stored at lower temperature compared to that of the original test in the protocol. Based on the expansion results, new expansion criteria are suggested that can detect if the aggregate contains oxidizable sulphides.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".