MétaCan
Menu
Back to cohort
Record W4360614192 · doi:10.1061/9780784484692.028

Three-Dimensional Spatial Stability Analysis of the Fundão Dam

2023· article· en· W4360614192 on OpenAlexaff
Gilson de Farias Neves Gitirana, João Paulo Tavares Souza, Nícolas Rodrigues Moura, Marina Trevizolli, M. D. Fredlund

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsSetbackRegular polygonSlip (aerodynamics)InstabilityGeologySlope stability analysisGeotechnical engineeringStability (learning theory)Shear (geology)GeometrySlope stabilityStructural engineeringMathematicsComputer scienceMechanicsPhysicsEngineering

Abstract

fetched live from OpenAlex

The failure of the Fundão Dam has been previously studied using two-dimensional approaches. The original investigation findings did not point to mechanisms of slope instability as the causes of the disaster. However, the complex shape of the dam and its concave and convex regions were not considered due to the limitations of the analysis tools employed. This paper presents two-dimensional (2D) and three-dimensional (3D) analyses that are more rigorous and better suited for the Fundão Dam geometry. Slope stability analyses were accomplished using the general limit equilibrium method (GLE). Critical slip surfaces were searched in 2D and 3D across the entire dam face. Drained shear strength parameters led to factors of safety (FS) that would indicate fairly stable conditions, with the lowest FS being 1.744. Values close to 1.0 were obtained considering undrained conditions. The 3D values of FS were very close to the 2D values, but with noticeably higher variations near the concave and convex regions. This leads to the conclusion that the dam shape near the setback region requires closer examination by means of 3D analyses, enabling the identification of critical zones.

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 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 score0.327
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicDam Engineering and SafetyFrench-language works237,207