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Record W2811479798 · doi:10.1139/cgj-2018-0082

Design procedure for landslide stabilization using sheet pile ribs

2018· article· en· W2811479798 on OpenAlexafffundvenueabout
James R. Bartz, C. Derek Martin, Michael T. Hendry

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaTransport Canada
KeywordsPileDeflection (physics)LandslideSheet pileGeotechnical engineeringGeologyStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

A design procedure was developed for a relatively unknown slope stabilization technique consisting of a series of parallel sheet piles installed parallel to the direction of slope movement. This technique was introduced in Alberta by R.M. Hardy in the 1970s and is locally referred to as “Hardy Ribs.” A case study is discussed where Canadian National (CN) Rail installed Hardy Ribs to stabilize a landslide affecting its rail line in western Manitoba. A proposed design procedure is discussed that consists of a de-coupled approach with a separate limit equilibrium slope stability analysis and laterally loaded pile analysis using p–y curves, where p is the soil reaction per unit length and y is the lateral deflection of the pile, to model the soil–pile interaction. Example calculations are provided for the proposed design procedure for the CN case study site to illustrate its use and to estimate the stabilizing effect from the Hardy Ribs at this site.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.022
GPT teacher head0.226
Teacher spread0.203 · 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 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

Citations8
Published2018
Admission routes4
Has abstractyes

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