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Record W7017885582

Constitutive Modeling of Flow Liquefaction of Tailings

2023· other· fr· W7017885582 on OpenAlexafffund

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2023
Typeother
Languagefr
Field
Topic
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLiquefactionDebitage
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: «RÉSUME: La liquéfaction par écoulement est la perte rapide de la résistance au cisaillement des sols granulaires saturés et lâche pendant un chargement non drainé. Ce phénomène est à l’origine de certaines des défaillances catastrophiques les plus récentes survenues dans les barrages de résidus miniers. Le potentiel de développement de ce mécanisme est difficile à évaluer en pratique car il est complexe et nécessite l’utilisation de modèles constitutifs avancés afin de le modéliser correctement. Cette recherche se concentre sur le développement d'un modèle constitutif pratique pour simuler la liquéfaction statique, compte tenu des caractéristiques des résidus miniers. En particulier, la teneur en fines des résidus miniers à l’intérieur des barrages à résidus présente une importante variation spatiale. Dans la littérature, des efforts ont été faits pour adapter les lois de comportement pour sable existants afin d'intégrer l'influence de la teneur en fines sur le comportement mécanique des mélanges sable-silt. Cependant, l’efficacité de cette approche dépend principalement de la performance du modèle pour sable choisi.. Cette thèse se concentre sur le modèle NorSand, qui constitue un choix privilégié parmi les professionnels pour simuler le comportement du sable. Il se distingue par l'utilisation efficace de seulement trois à quatre paramètres. Les paramètres restants du modèle, qui sont utilisés pour la définition du module d'élasticité et de l'état critique, partagent des concepts fondamentaux avec ceux utilisés par d'autres modèles de sol avancés basés sur la mécanique des sols à l'état critique. L'objectif principal de cette thèse est d'étendre les fonctionnalités du modèle NorSand pour couvrir la simulation de la liquéfaction statique des résidus caractérisés par une teneur variable en fines. Tout au long de ce processus, l'intention demeure de préserver la simplicité et la praticité inhérentes au modèle.» ABSTRACT: «ABSTRET:Flow liquefaction is the rapid loss of shear strength of loose saturated granular soils under undrained loading. This phenomenon has been responsible for some of the most recent catastrophic failures that happened in tailings dams. The potential for this mechanism to develop is hard to evaluate in practice because it is complex and requires the use of advanced constitutive models. This research focuses on developing a practical constitutive model for proper simulation of static liquefaction, considering the unique characteristics of tailings. Particularly, one prominent characteristic of tailings within a tailings dam is the spatial variation in fines content throughout the dam structure. In the literature, efforts have been made to adapt existing sand models to incorporate the influence of fines content on the mechanical behavior of sand-silt mixtures. Yet, the effectiveness of the proposed framework mainly depends on the selection of an appropriate sand-based model for extension. This thesis notably focuses on the NorSand model, which is a preferred choice among professionals for simulating sand behavior. It stands out due to its efficient utilization of just three to four material constants. The model's remaining parameters, which are used in the elastic modulus and critical state definition, share fundamental concepts as those employed by other advanced critical state-based soil models. The primary objective is to expand the functionality of the NorSand model to cover the simulation of static liquefaction in tailings materials characterized by varying fines content. Throughout this extension process, the intention remains to preserve the model's inherent simplicity and practicality.»

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)0.000
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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.242
Teacher spread0.226 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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