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Record W2107220068 · doi:10.1139/cgj-2013-0297

Penetrability of microfine cement grouts: experimental investigation and fuzzy regression modeling

2014· article· en· W2107220068 on OpenAlexvenueno aff
Ioannis N. Markou, D. N. Christodoulou, Basil Papadopoulos

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersGeneral Secretariat for Research and TechnologyEuropean Commission
KeywordsCementFinenessMaterials scienceSuspension (topology)Penetration (warfare)Geotechnical engineeringComposite materialLinear regressionGradationMineralogyGeologyMathematics

Abstract

fetched live from OpenAlex

One-dimensional injection tests were conducted on sand columns with a height of 134 cm for the penetrability evaluation of microfine cement grouts. Three ordinary cement types were pulverized to obtain microfine cements having nominal maximum grain sizes of 20 and 10 μm, and these cements were used in the present investigation. Suspensions with water to cement (W/C) ratios of 1, 2, and 3, by weight, were injected into 13 clean sands with d 15 ranging from 0.17 to 2.25 mm and C u ranging from 1.19 to 6.67. Pulverization of the ordinary cements to produce microfine cements extends the range of groutable sands to “medium-to-fine”. Cement fineness; suspension W/C ratio and apparent viscosity; and sand grain size, gradation, and relative density are very important parameters, as they substantially affect both grout penetration and maximum injection pressure. The penetration length of cement grouts was correlated to parameters pertinent to the suspension, sand, and injection process by performing fuzzy and ordinary linear regression analyses of the injection test results. The resultant fuzzy regression models provided successful penetration length predictions for the majority of the cases analyzed, while the best ordinary regression model exhibited a correlation coefficient not higher than 0.363.

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 score0.537
Threshold uncertainty score0.471

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 head0.212
Teacher spread0.196 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

Explore more

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