ACCELERATED SCREENING TESTS FOR ALKALI AGGREGATE REACTIONS IN HONG KONG
Bibliographic record
Abstract
Some 50% of the aggregates used for construction in Hong Kong are imported, mostly from nearby quarries in Mainland China. While local aggregates produced from granite are generally regarded as non-reactive with respect to alkali aggregate reaction (AAR), less can be said about imported aggregates. Structures suffering from the effects of AAR have been reported, investigated and confirmed by the Public Works Central Laboratory (PWCL). Accelerated mortar bar test (AMBT) methods were used at the PWCL for testing the alkaline aggregate reactivity of aggregates, including imported aggregates and recycled aggregates. The results of these tests using four different AMBT methods, originated from Hong Kong, the US, Canada and Europe, are presented and discussed in this paper. This paper also describes a study which compares the results of AMBT using high a kaline cement in forming the mortar bars with those using low alkaline cement but with sodium hydroxide or sodium chloride added. The International Union of Testing and R search Laboratories for Materials and Structures (RILEM) method was used for the AMBT. The differences in the results are highlighted and discussed. The study also investigated the role of alkaline in the cement, and of the sodium ions and hydroxide ions added to the mortar mix during the AMBT in the development of AAR. INTRODUCTION OCCURRENCE OF AAR IN HONG KONG ASSESSMENT OF AGGREGATES BY ACCELERATED MORTAR BAR TEST RESULTS AND DISCUSSION CONCLUSIONS AND RECOMMENDATIONS ACKNOWLEDGEMENT REFERENCES
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".