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Record W2973059415 · doi:10.1617/s11527-019-1396-z

RILEM TC 247-DTA round robin test: mix design and reproducibility of compressive strength of alkali-activated concretes

2019· article· en· W2973059415 on OpenAlexafffund
John L. Provis, K. Arbi, Susan A. Bernal, Dali Bondar, Anja Buchwald, Arnaud Castel, Sundararaman Chithiraputhiran, Martin Cyr, Alireza Dehghan, Katja Dombrowski-Daube, Ashish Dubey, Vilma Ducman, Gregor J. G. Gluth, Sreejith Nanukuttan, Karl Peterson, F. Puertas, Arie van Riessen, Manuel Torres‐Carrasco, Guang Ye, Yibing Zuo

Bibliographic record

VenueMaterials and Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilMaterials innovation instituteUniversité de ToulouseJavna Agencija za Raziskovalno Dejavnost RSQueen's UniversityCurtin University of TechnologyNederlandse Organisatie voor Wetenschappelijk OnderzoekTechnische Universiteit Delft
KeywordsDurabilityMetakaolinCompressive strengthRound robin testMaterials scienceFly ashGround granulated blast-furnace slagAluminosilicateGeopolymerComposite materialChemistryMathematics

Abstract

fetched live from OpenAlex

Abstract The aim of RILEM TC 247-DTA ‘Durability Testing of Alkali-Activated Materials’ is to identify and validate methodologies for testing the durability of alkali-activated concretes. To underpin the durability testing work of this committee, five alkali-activated concrete mixes were developed based on blast furnace slag, fly ash, and flash-calcined metakaolin. The concretes were designed with different intended performance levels, aiming to assess the capability of test methods to discriminate between concretes on this basis. A total of fifteen laboratories worldwide participated in this round robin test programme, where all concretes were produced with the same mix designs, from single-source aluminosilicate precursors and locally available aggregates. This paper reports the mix designs tested, and the compressive strength results obtained, including critical insight into reasons for the observed variability in strength within and between laboratories.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.016
GPT teacher head0.243
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations79
Published2019
Admission routes2
Has abstractyes

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