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Record W2800829825 · doi:10.7939/r3d795q97

Landslide Mitigation Using Granular Shear Keys: Observations from a Review of 38 Case Studies

2017· review· en· W2800829825 on OpenAlexaboutno aff
Hugh F Gillen

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyLandslideShear (geology)Geotechnical engineeringPetrology

Abstract

fetched live from OpenAlex

Canadian transportation industries have been using granular shear keys to remediate shallow slope instabilities for decades. Engineers have used limit equilibrium (LE) analyses as the standard method for designing these granular shear keys (Wyllie & Mah, 2004). These LE analyses do not account for the strain required to mobilize the shear resistance in the granular backfill. Finite element methods can model deformation but require calibration and may not always be practical for many applications. Recommendations featuring plots and/or nomograms based on a review of the design and resulting performance of an extensive collection of case histories could supplement current design methods and enhance design effectiveness and reliability. This thesis presents a compilation of 38 case studies for trenched granular shear keys or rockfill column shear keys. The purpose of this compilation was to identify empirical relationships between shear key design and deformation, to enhance the economy and performance of future granular shear keys. Existing granular shear key design guidelines were reviewed and compiled. Case studies featuring granular shear keys were then collected. Details from these case studies were summarized and data was compiled into a database. The data was analyzed and compared with recommendations from the existing guidelines. Additional analyses using deformation data yielded predictions for the magnitude and timespan of post-construction deformation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.069
GPT teacher head0.280
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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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