Reaction Kinetic Studies of the Electrical Conductivity in Dissolution Experiments for the Identification of Alkali-Reactive Aggregate
Bibliographic record
Abstract
The alkali-silica reaction (ASR) is still a significant damage process in concrete structures.During an ASR poorly crystalline aggregate reacts with alkalis to form an alkali-silica gel.The alkali-silica gel causes damage by expansion within the concrete structure through the absorption of water.Since stopping an ASR is often not possible, costly and, time-consuming, preventive measures are taken and the use of reactive aggregates is avoided.Various test methods for the identification of alkali reactive aggregates already exist worldwide.However, there is currently no test method that can evaluate an aggregate in the short term without prior analysis, as in the case of an incoming goods inspection.In the Institute of Materials, Physics and Chemistry of Buildings at Hamburg University of Technology (TUHH) a new fast test method to identify alkali-reactive aggregates is being worked on.The test method is an accelerated dissolution experiment with ground aggregate like chemical testing methods, such as ASTM C289.But in the new approach, the reactivity of the aggregate is derived from the change in several chemical parameters due to a dissolution process.The dissolution process of the aggregate takes place in hot alkaline solution with an excess of alkalis.During the experiment, the pH-value, the electrical conductivity, and the redox potential are recorded as a function of time.Conspicuous points, such as extreme points and stationary states of these functions are to be used to evaluate the reactivity of the aggregate.It is assumed that the alkali reactivity of the aggregates is essentially reflected in the chemical parameters and thus, in reverse, a specific evaluation of the functions could provide indications of the reactivity of the aggregate.In this paper we present results regarding reaction kinetics and show possibilities to use this new method as an indicator for the reactivity of an aggregate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".