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Record W2765978075 · doi:10.1680/jmacr.17.00090

Correlating the permeability of mortar under compression with connected porosity and tortuosity

2017· article· en· W2765978075 on OpenAlexafffund
Vivek Bindiganavile, Muhammad Abdullah Al Mamun, Behrang Dashtestani, Nemkumar Banthia

Bibliographic record

VenueMagazine of Concrete Research · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCentre for Transportation Engineering and PlanningCement Association of Canada
KeywordsTortuosityPorosityMaterials scienceMortarComposite materialPermeability (electromagnetism)Compressive strengthCompression (physics)Geotechnical engineeringGeology

Abstract

fetched live from OpenAlex

This paper describes the influence of pore structure parameters – connected porosity and tortuosity – on the water permeability coefficient of cement mortar. Plain and fibre-reinforced mortar specimens were prepared to evaluate the permeability and characterise the microstructure. Polypropylene microfibres were incorporated at 0·25% volume fraction in the fibre-reinforced mortar specimens. The permeability of the mortar specimens was determined using an in situ method where hollow cylinders were subjected to uniaxial compressive stress. The specimens were loaded in the range of 0–90% of the ultimate compressive strength in five different stages. X-ray computed micro-tomography was employed to characterise the pore system of the mortar specimens. By analysing the tomographic images, both connected porosity and tortuosity of the pore network were evaluated. The results showed that the coefficient of water permeability has a correlation with connected porosity across the load levels. While no significant correlation was found with tortuosity alone, the water permeability was found to depend on the ratio between connected porosity and tortuosity as expressed by the Kozeny–Carman formulation.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.057
GPT teacher head0.326
Teacher spread0.269 · 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 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

Citations15
Published2017
Admission routes2
Has abstractyes

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