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Record W2089889059 · doi:10.1139/l01-003

Measurements of laboratory rates of concrete expansion and their comparisons with field rates

2001· article· en· W2089889059 on OpenAlexfundvenueaboutno aff
A. M. Hefny, K. Y. Lo, L. Adeghe

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlkali–aggregate reactionAggregate (composite)Reliability (semiconductor)Geotechnical engineeringStructural engineeringLaboratory testEnvironmental scienceField (mathematics)RebarResidualInstrumentation (computer programming)EngineeringForensic engineeringComputer scienceMaterials scienceMathematicsComposite material

Abstract

fetched live from OpenAlex

The R.H. Saunders dam is one of over one hundred concrete dams, worldwide, suffering from structural and operational problems due to the expansion of concrete resulting from alkali–aggregate reaction. A laboratory test methodology for measuring the residual expansion rate in these dams with simulation of field environment has been developed. This paper describes the test principles and method of interpretation of the different tests developed. Results of various expansion tests performed on samples recovered from the R.H Saunders dam (Canada) are presented. Results showed that the expansion rates measured in laboratory are consistent with those measured by extensive instrumentation in the field (in situ rebar tests, levelling data, stress meters, and in situ overcoring data). It is believed that the test methodology developed provides the necessary and inexpensive tools for measuring the stress-dependent residual expansion potentials in concrete dams. It would also have an impact on the design of remedial measures and the prediction of future performance because of the reliability and versatility of the test method.Key words: dams, concrete, expansion, alkalis, aggregate, reaction, cement.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.193
Teacher spread0.178 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2001
Admission routes3
Has abstractyes

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