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Record W2608502673 · doi:10.1139/cgj-2016-0671

Probabilistic investigations on the watertightness of jet-grouted ground considering geometric imperfections in diameter and position

2017· article· en· W2608502673 on OpenAlexvenueno aff
Yutao Pan, Yong Liu, Jun Hu, Miaomiao Sun, Wei Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversity of California, San DiegoNational Science Foundation
KeywordsSlabStructural engineeringMonte Carlo methodJet (fluid)Position (finance)Geotechnical engineeringFlow (mathematics)Volumetric flow rateEngineeringMechanicsMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

The effect of geometric imperfections in both diameter and position of jet-grouted columns on the watertightness of an underground cement-treated slab is investigated in this study. A three-dimensional discretized algorithm is proposed to facilitate the detection and measurement of untreated zones that penetrate the treated slab. The normalized flow rate of a cement-treated slab is then evaluated by calculating the harmonic average area of the penetrated defect. Statistical evaluation of the gross flow rate through the penetrated defects is carried out via Monte Carlo simulations. The results show that a more economic design is obtainable if intracolumn variation of diameter is considered or multi-shaft jet-grouting is used. Based on the statistical results, a reliability-based design method is proposed for designers to strike a balance among various design parameters, including slab thickness, depth, column diameter, and column spacing.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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 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

Citations30
Published2017
Admission routes1
Has abstractyes

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