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Record W4412024261 · doi:10.1139/cgj-2024-0755

Requirements of efficient deep soil mixing treatment in clayey soils: a field-based assessment of water predrilling and auger free blade effects

2025· article· en· W4412024261 on OpenAlexvenueno aff
Seyed Meisam Alavi, Milad Aghamolaei, Sajjad Shakeri Talarposhti, Ahmad Ali Khodaei Ardabili

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSoil waterBlade (archaeology)DrillingGeologyClay soilAugerMixing (physics)Environmental scienceSoil scienceEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Achieving a uniform/high-strength deep soil mixed (DSM) column and avoiding sticking cohesive soils to blades (i.e., entrained mixing/rotation phenomenon) requires multi-disciplinary involvement, including drilling tool configuration and mix designs. A series of 80 cm diameter DSM columns was executed in high cohesive clays using various drilling auger formations containing different numbers of free blades and with/without water predrilling phases. Data interpretation was combined with full-depth coring and rig sensor records. The outcomes highlighted that adding free blades to the auger in a proper formation (dimensions/placement/shape/stiffness) resulted in uniform columns and facilitated the drilling by reducing the drilling pressure by about 40% while all the parameters were the same. The required water discharge in the predrilling phase was formulated to aim for a water content (about 46%) beyond the liquid limit of clayey deposits (21%–44%): a decisive technique to facilitate drilling in stiff cohesive soils. A 100% increase in the strength and a 50% enhancement in uniformity indexes in the executed columns of this project were achieved only through a tuned free-blade auger (number/configuration) and an optimal amount of added water. Besides, simultaneously implementing the predrilling phase and free blades to maximize drilling quality was inevitable due to their intertwined functions. Moreover, a new practical equation has also been proposed to consider the effect of free blades on the BRN within cohesive soil layers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.992

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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 designObservational
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

Citations4
Published2025
Admission routes1
Has abstractyes

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