Use of the Accelerated Mortar Bar Test to Evaluate the Effectiveness of LiNO3 Against Alkali-Silica Reaction—Part 1: Pore Solution Chemistry and Influence of Various Parameters
Bibliographic record
Abstract
Abstract For the time being, the only reliable test method to evaluate the effectiveness of lithium nitrate against alkali-silica reaction (ASR) in concrete is the concrete prism test (CPT) CSA A23.2-14A or ASTM C1293, extended to two years. In its actual form, the more commonly used accelerated mortar bar test (AMBT) CSA A23.2-25A or ASTM C1260 is not able to predict this effectiveness and needs to be modified to improve it’s reliability. Part I of this study, which involves a large variety of reactive aggregates from Canada and the United States, aims to evaluate the partition of various ions (OH−, Na+, K+, Li+, and silica species) between the mortar pore solution and the soak solution in the AMBT, and the effect of a number of experimental parameters on the expansion of mortar bars with/without lithium nitrate, e.g., the presence of lithium in the original mortar bars, the Li concentration in the soak solution, the concentration and the composition (NaOH versus KOH) of the soak solution, the cement alkali content, the water-to-cement ratio, and the initial 24-h soaking in pure water. The second part of this study (Part II) compares the above AMBT results with the CPT results for the same aggregates and aims to propose modifications to the AMBT when testing lithium nitrate against ASR to better correlate with the most realistic CPT results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".