Bibliographic record
Abstract
Papers published in Construction Materials are eligible for awards from the Institution of Civil Engineers. Papers from any of the ICE journals can be nominated for several awards. In addition, each journal has awards dedicated to their specific subject area.On Friday 13 October 2023, ICE president Keith Howells presented an award to the following paper published in Construction Materials in 2022. The editorial panel nominated their best papers and an awards committee chaired by Tim Broyd allocated the awards.The Thomas Howard Medal, presented for the best paper detailing the use of materials in construction, was awarded to Hooton and Fournier (2022).In this study, the impact of high-alkali Portland cements on the prescribed level of supplementary cementitious materials (SCMs) required in the Canadian standard for alkali–silica reaction mitigation was evaluated. On the basis of the results, for concretes containing aggregates exhibiting moderate reactivity, the maximum allowable cement alkali limit was increased from 1.00 to 1.15%. For all the levels of aggregate reactivity, cement alkali contents could be allowed up to 1.25% provided the recommended level of mitigation by SCMs was increased. In the initial laboratory study, mortar bars and concrete prisms were cast and monitored using two different reactive aggregates and recommended levels of fly ash and slag. For the concrete prism tests, the alkali contents of cements were increased to 1.25%, as per the standard, or were increased by 0.25%. Instrumented outdoor exposure concrete blocks, along with additional concrete prisms stored at different temperatures, were cast from numerous mixtures prepared with cement alkali equivalents ranging up to 1.22%. This paper reports on the long-term performance of prisms and concrete blocks after 12 and 27 years. The performance of the outdoor blocks is also compared with the predicted performance based on the results of accelerated mortar bar and concrete prism test.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.440 | 0.368 |
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".