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Record W2620558462 · doi:10.1061/9780784480700.025

Selecting Minimum Factors of Safety for 3D Slope Stability Analyses

2017· article· en· W2620558462 on OpenAlex
Timothy D. Stark, Daniel Ruffing

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeo-Risk 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsKensington Health
Fundersnot available
KeywordsSlope stabilityStability (learning theory)Environmental scienceGeologyMathematicsComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

Geotechnical engineers understand there is uncertainty and risk in the input parameters for slope stability analyses and within the analysis methodologies themselves. Decades of research and inverse analyses of slope failures have resulted in widespread acceptance of certain factors of safety (FS) in typical situations, e.g., a static two-dimensional (2D) factor of safety of 1.3 is often used for temporary or low risk slopes and 1.5 for permanent slopes. However, these FSs are not appropriate for use with three-dimensional (3D) analyses because 3D analyses account for additional shear resistance that is generated along the sides of the slide mass. The contribution of the additional shear resistance can be significant in shallow slide masses or for translational slide masses with a width to height ratio less than six, resulting in calculated values of 3D FS that are greater than the calculated 2D FS. To achieve the same level of safety or risk as a static 2D FS of 1.3 or 1.5, the user must use a greater minimum FS for 3D analyses. This paper presents methods for calculating a suitable minimum 3D FS to achieve a similar level of safety or risk as a minimum 2D FS, such as 1.3 or 1.5, would afford.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.048
GPT teacher head0.297
Teacher spread0.249 · 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