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Record W2730996084 · doi:10.1061/9780784480809.010

Construction of a Jet-Grouted Backup Seepage Cut-Off Wall in the John Hart North Earthfill Dam

2017· article· en· W2730996084 on OpenAlexafffund
Paolo Gazzarrini, David Siu, Stephen Jungaro

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsGeoscience BCBC Hydro (Canada)
FundersBC Hydro
KeywordsGroutDrawdown (hydrology)Geotechnical engineeringJackingLeveeAbutmentCofferdamEmbankment damJet (fluid)Foundation (evidence)GeologyDewateringEngineeringCivil engineeringGroundwaterAquifer

Abstract

fetched live from OpenAlex

To provide additional protection to a 70 year-old dam in case of a seismic event, the local utility company, BC Hydro, required the construction of a backup seepage cut-off wall in the right abutment of the John Hart North Earthfill Dam, using jet-grouting, a well-known technique that uses a high-velocity jet of grout mix at high pressure (300 to 500 bars) to erode and simultaneously mix cementitious grout with the soil in situ. The main challenge was the use of these high pressures during construction of the seepage cut-off wall in the dam fills and foundation soil while avoiding reservoir drawdown, to minimize the risk of hydro-fracturing or hydro-jacking in the embankment. The contractor carried out a field trial in a safe area with similar soil conditions and, with the utility, developed a jet-grouting procedure that minimized the risks. The paper describes the jet-grouting procedures, grout mix used, results of the field trial, and safety and environmental precautions implemented on site to reach successful completion.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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 designNot applicable
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

Citations3
Published2017
Admission routes2
Has abstractyes

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