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Record W2724589029 · doi:10.1061/9780784480809.004

An Unconventional Application of Jet Grouting to Install 4900 kN Ground Anchors in Loose Alluvial Soil

2017· article· en· W2724589029 on OpenAlexaff
Vittorio Manassero

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsGeotechnical engineeringJet (fluid)AlluviumSection (typography)GeologyStructural engineeringEngineeringComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

This paper addresses an unconventional application of jet grouting, where improved soil columns acted as the bonding section of ground anchors installed in loose alluvial soil with an unusually high capacity. A retaining wall, 25-m high, which had been constructed to support refuse and waste material, began to suffer displacements that became so large as to reach very critical conditions in terms of stability safety. To improve its stability 120 ground anchors were installed, each capable of withstanding a 4900 kN working load. The bonding section was achieved by a 1.5-m diameter jet-grouting column. Suitability tests were performed to confirm the adequacy of the design and acceptance tests were carried out on all the ground anchors, both at a loading force of 5900 kN. All the anchors performed very well, showing good elastic behavior at each loading level and no symptoms of incipient failure.

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 score0.004

Distilled classifier scores by category (both heads)

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

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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