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Record W2320348399 · doi:10.1061/40663(2003)123

Numerical Simulation of Chemical Grouting in Heterogeneous Porous Media

2003· article· en· W2320348399 on OpenAlexaff
Tirupati Bolisetti, Stanley Reitsma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsGroutHydraulic conductivityMODFLOWPorous mediumGeotechnical engineeringMaterials scienceConductivityComputer simulationPorosityMechanicsGeologyGroundwaterSoil scienceSoil waterGroundwater flowAquiferChemistryPhysics

Abstract

fetched live from OpenAlex

A mathematical model to simulate chemical grout injection, grout curtain formation in aiding rational design of chemical grout systems in saturated porous media is proposed. Three-dimensional modular groundwater flow simulation model (MODFLOW) and three-dimensional multi-species reactive transport (RT3D) model are combined and modules for the gelling process are incorporated to simulate the grouting process. The paper investigates the influence of varying degrees of soil heterogeneity and layering on grout barrier formation through numerically generated hydraulic conductivity fields. Layer persistence and range of conductivity can be specified during generation of conductivity fields. Grout barrier performance is assessed by simulation of grout injection in a three-dimensional domain followed by determination of post-grouted conductivity field and calculation of overall grout curtain hydraulic conductivity using a flow model. The simulation results show that spatial variability control the effectiveness of grout curtain performance. About 80–90% of the 25 hydraulic conductivity fields with high variability failed to reach the desired effective hydraulic conductivity.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.385

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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

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