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Record W2502099092 · doi:10.1680/jgrim.14.00016

Optimising deep mixed soil zones in land reclamation projects

2016· article· en· W2502099092 on OpenAlexaff
Sylvia L. Bryson, Hany El Naggar, Arun J. Valsangkar

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Ground Improvement · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsDalhousie UniversityUniversity of New Brunswick
Fundersnot available
KeywordsLand reclamationEnvironmental scienceWork (physics)Water tableCivil engineeringHydrology (agriculture)Geotechnical engineeringGeologyGroundwaterEngineeringGeography

Abstract

fetched live from OpenAlex

The use of deep soil mixing technologies has become increasingly common in North America following its widespread development and adoption in Japan and Scandinavia. This research project considered how design of soft soils stabilised with deep soil mixing may be optimised to satisfy global slope stability requirements to assist the construction of large reclaimed land projects. The land reclamation case that was studied in the present paper deals with raising the elevation of a terrestrial area above a tidal or flood zone, where the water table is close or at the original grade. In this work, the effect of several key design parameters on the global factor of safety against slope failure for a land reclamation project was studied using limit equilibrium slope stability analyses. The parametric analyses considered design parameters, including the thickness of the soft soil layer, as well as the strength, width and location of a stabilised soil zone. The purpose of this work is to provide guidelines for the preliminary design of ground improvements for future land reclamation projects using deep soil mixing methods by identifying design parameters that have the most significant impact on the global factor of safety.

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 score0.199
Threshold uncertainty score0.489

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 head0.181
Teacher spread0.173 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Civil Engineers - Ground ImprovementSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207