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Record W2997229577 · doi:10.1520/jte20180114

Response of Concrete to Incremental Aggression of Sulfuric Acid

2018· article· en· W2997229577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSulfuric acidSilica fumeFly ashPortland cementCementitiousMaterials scienceCementComposite materialChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract There is lack of standardized tests for specifically evaluating the resistance of concrete to sulfuric acid attack, which has caused great variability, for example, in terms of solution concentration, pH level/control, etc., among previous studies in this area. Accordingly, there are conflicting data about the role of key constituents of concrete (e.g., supplementary cementitious materials [SCMs]) and uncertainty about building codes’ stipulations for concrete exposed to sulfuric acid. Hence, the aim of this study was to assess the behavior of the same concretes, prepared with single and blended binders, to incremental levels (mild, severe, and very severe) of sulfuric acid solutions over 36 weeks. The test variables included the type of cement (general use [GU] or portland limestone cement [PLC]) and SCMs (fly ash, silica fume, and nanosilica). The severe (1 %, pH of 1) and very severe aggression (2.5 %, pH of 0.5) phases caused mass loss of all specimens, with the latter phase providing clear distinction among the performance of concrete mixtures. The results showed that the penetrability of concrete was not a controlling factor under severe and very severe damage by sulfuric acid attack, whereas the chemical vulnerability of the binder was the dominant factor. Mixtures prepared from the PLC performed better than those prepared from GU. While the quaternary mixtures that consisted of GU or PLC, fly ash, silica fume, and nanosilica showed the highest mass losses after 36 weeks, the binary mixtures incorporating GU or PLC with fly ash had the lowest mass losses.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.343
Teacher spread0.276 · 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