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Record W2915507587 · doi:10.1139/cgj-2017-0544

Probabilistic seismic slope stability analysis and design

2019· article· en· W2915507587 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeotechnical engineeringProbabilistic logicSeismic loadingSlope stabilityStability (learning theory)Seismic analysisSlope stability analysisProbabilistic analysis of algorithmsGeologyStructural engineeringEngineeringMathematicsComputer scienceStatistics

Abstract

fetched live from OpenAlex

Deterministic seismic slope stability design charts for cohesive–frictional ([Formula: see text]) soils are traditionally used by geotechnical engineers to include the effects of earthquakes on slopes. These charts identify the critical seismic load event that is sufficient to bring the slope to a state of limit equilibrium, but they do not specify the probability of this event. In this paper, the probabilistic seismic stability of slopes, modeled using a two-dimensional spatially random [Formula: see text] soil, is examined for the first time using the random finite element method (RFEM). Slope stability design aids for seismic loading, which consider spatial variability of the soil, are provided to allow informed geotechnical seismic design decisions in the face of geotechnical uncertainties. The paper also provides estimates of the probability of slope failure without requiring computer simulations. How the design aids may be used is demonstrated with an example of slope remediation cost analysis and risk-based design.

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)0.001
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.817
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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