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Record W1613333849 · doi:10.1139/cgj-2012-0199

Investigation of field-installation effects of horizontal twin-jet grouting in Shanghai soft soil deposits

2013· article· en· W1613333849 on OpenAlexvenueno aff
Zhi-Feng Wang, Shui‐Long Shen, Chu-Eu Ho, Yong‐Hyun Kim

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsGeotechnical engineeringJet (fluid)Pore water pressureGeologySoil waterShear (geology)Lateral earth pressureRADIUSGroundwaterEngineeringSoil science

Abstract

fetched live from OpenAlex

This paper presents a case study of an investigation into the responses of the surrounding ground to the horizontal twin-jet grouting method (HTJGM) in soft soil deposits of Shanghai. During the field test, the variation of pore-water pressure, lateral earth pressure, lateral displacements of the subsurface soils, and ground surface heave induced by the installation of five horizontal jet-grouted columns were monitored. The monitoring results indicate that the excess pore-water pressure reached 4 to 6 times the undrained shear strength of the soils, while maximum lateral displacements and ground surface heave were up to 80 and 17 mm, respectively. The influence range due to the installation of jet-grouted columns was between 15 and 20 times the nominal column radius. The development of prediction methods for lateral displacements and ground surface heave induced by the HTJGM installation process are presented and discussed. Results from the investigation suggest that the proposed prediction methods can be used to provide reasonable estimates of ground response and influence range of horizontal jet grouting.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.170
Teacher spread0.165 · 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

Citations142
Published2013
Admission routes1
Has abstractyes

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