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Record W4404659485 · doi:10.3208/jgssp.vol11.ds-4-01

Some experiences from the introduction of wet deep mixing in Scandinavia

2024· article· en· W4404659485 on OpenAlexaff
Sølve Hov, Håkan Eriksson, Tony Forsberg, Kristina Borgström

Bibliographic record

VenueJapanese Geotechnical Society Special Publication · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsNordic Life Science Pipeline (Canada)
Fundersnot available
KeywordsGeotechnical engineeringMixing (physics)GeologyForensic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The dry deep mixing (DDM) method has been used extensively – and exclusively – in the Nordic countries since its development in the 1970s. Despite being a versatile method, it is mainly limited to sensitive clays with low shear strengths and the method has limitations due to considerable verticality deviations. However, increasing ground improvement needs for stiffer clays, improved homogeneity, and better verticality at greater depths has led to the introduction of the wet deep mixing method (WDM) in both Sweden and Norway. This paper summarizes some experiences from three projects where WDM recently have been employed both onshore and offshore to increase stability and reduce settlements. Two Swedish projects, one large scale commercial and one trial test, were performed in clays with low shear strengths and high water contents for stability purposes offshore for land reclamation. The Norwegian project involved improvement of a low water content clay for settlement reduction of a building foundation. Results from strength verification using wet grab and core sampling are presented and are compared to experiences from DDM and preceding laboratory tests. The paper furthermore discusses some practical issues around execution and strength verification in the three projects. Overall, it is concluded that WDM in many cases is suitable also for Scandinavian clays and can therefore replace or supplement DDM. However, the lack of experience still calls for certain conservatism. Trial columns are recommended until there is more field experience from WDM in Scandinavian clays.

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.231
Teacher spread0.223 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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