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Record W2783797474 · doi:10.1139/cgj-2017-0412

New approach of vacuum preloading with booster prefabricated vertical drains (PVDs) to improve deep marine clay strata

2018· article· en· W2783797474 on OpenAlexvenueno aff
Yuanqiang Cai, Zhiwei Xie, Jun Wang, Peng Wang, Xueyu Geng

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBooster (rocketry)Geotechnical engineeringEngineeringDrainageLand reclamation

Abstract

fetched live from OpenAlex

This paper presented a new approach for ground improvement of deep marine clay in which the conventional booster tube in the current air booster vacuum preloading technology was replaced by a booster prefabricated vertical drain (PVD). In comparison to the ordinary PVD, the booster PVD could provide inflow channels for the compressed air when the booster pump was in operation. To examine the performance of this new air booster vacuum preloading technology, in situ field tests were conducted at Oufei sluice project in Wenzhou, China, where the thickness of the soft soil layers (i.e., marine clay) was more than 20 m. An extensive monitoring system was implemented to measure the vacuum pressure, pore-water pressure, settlement, and lateral displacement at this reclamation site. With the collected field monitoring data, a comprehensive data analysis was carried out to evaluate the extent of ground improvement. The study results depicted that this new air booster vacuum preloading technology was more effective for the ground improvement of the deep marine clay layers, in comparison to the conventional vacuum preloading technology.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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 designBench or experimental
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

Citations189
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207