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Record W7062403716

Validation expérimentale d'un modèle semi-empirique pour la prédiction de la résistance mécanique des remblais rocheux cimentés : Une nouvelle approche de contrôle qualité

2024· other· fr· W7062403716 on OpenAlexfundaboutno aff

Bibliographic record

VenueDepositum (Université du Québec en Abitibi-Témiscamingue) · 2024
Typeother
Languagefr
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersFondazione Italiana per la Ricerca sul CancroFondation de l’Université du Québec en Abitibi-TémiscamingueNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsContext (archaeology)Rail transportationDecantation
DOInot available

Abstract

fetched live from OpenAlex

Résumé Le domaine minier constitue un pilier essentiel pour l'économie de la province de Québec, contribuant de manière significative à son PIB. Cependant, cette industrie génère d'importantes quantités des déchets solides, notamment les roches stériles (RS) et les résidus miniers (RM), qui posent un défi environnemental notable. Pour résoudre cette problématique, une solution adoptée est la réutilisation de ces déchets comme matériaux de remblai, donnant naissance à différents types de remblais miniers, dont les remblais rocheux cimentés (RRC). Leur appréciation croissante dans l'industrie s'explique par leur robustesse et leur capacité à assurer la stabilité des sites miniers grâce à une résistance à la compression (UCS) élevée. Toutefois, l'application des RRC reste complexe en raison de la variabilité de leurs caractéristiques et de la complexité liée à leur préparation, sans compter l'absence de méthodes de conception standardisées. Cette recherche vise à mettre au point une méthode de contrôle qualité technique pour les mélanges de RRC, s'appuyant sur une évaluation précise de leur résistance mécanique. L'étude s'articule autour de l'utilisation d'un modèle semi-empirique existant, conçu pour estimer l'UCS des RRC à partir des propriétés de leurs composants. La phase suivante de la recherche consiste en la validation et le calibrage de ce modèle grâce à l'analyse de données existantes et à l'expérimentation de nouvelles compositions en laboratoire. Cette étape cruciale conduit au développement d'une application dédiée à la prédiction des constantes spécifiques au type de ciment utilisé, facilitant ainsi l'adoption du modèle semi-empirique à large échelle. En complément, l'étude développe une procédure de contrôle qualité pour les mélanges de RRC. Cette démarche inclut la collecte et l'analyse des miettes d'éprouvettes pour déterminer leur teneur en eau gravimétrique, un paramètre déterminant pour évaluer divers indicateurs de qualité tels que la densité humide et sèche, ainsi que le taux de porosité des remblais. Les résultats obtenus montrent l'efficacité du modèle semi-empirique dans la prédiction de l'UCS des RRC, avec des coefficients de corrélation proches de 1, démontrant la robustesse et la fiabilité du modèle semi empirique étudié. En conclusion, cette recherche souligne l'importance de surveiller attentivement les caractéristiques géotechniques et de choisir avec soin les composants des remblais pour garantir non seulement leur performance mais également leur durabilité et leur stabilité environnementale. Abstract The mining sector is a crucial pillar for the economy of the province of Quebec, significantly contributing to its GDP. However, this industry generates substantial amounts of solid waste, particularly waste rocks (RS) and mining residues (RM), posing a significant environmental challenge. To address this issue, a solution has been adopted involving the reuse of these wastes as backfill materials, leading to various types of conventional mining backfills, including Cemented Rockfills (CRF). Their growing appreciation in the industry is due to their robustness and their ability to ensure the stability of mining sites through high uniaxial compressive strength (UCS). Nevertheless, the application of CRF remains complex due to the variability of their characteristics and the complexity associated with their preparation, not to mention the absence of standardized design methods. This research aims to develop a technical quality control method for CRF mixtures, based on an accurate evaluation of their mechanical resistance. The study focuses on the use of an existing semi-empirical model, designed to estimate the UCS of CRF based on the properties of its components. The first phase of the research involves validating and calibrating this model through the analysis of existing data and experimentation with new mixtures in the laboratory. This crucial step leads to the development of an application dedicated to predicting constants specific to the type of cement used, thus facilitating the adoption of the semi-empirical model on a large scale. Additionally, the study develops a quality control procedure for CRF mixtures. This approach includes the collection and analysis of specimen crumbs to determine their gravimetric water content, a crucial parameter for evaluating various quality indicators such as wet and dry density, as well as the porosity rate of the backfills. The results obtained demonstrate the effectiveness of the semi-empirical model in predicting the UCS of CRF, with correlation coefficients close to 1, showcasing the robustness and reliability of the studied semi-empirical model. In conclusion, this research highlights the importance of closely monitoring geotechnical characteristics and carefully selecting the components of backfills to ensure not only their performance but also their durability and environmental stability.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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 designBench or experimental
Domainnot available
GenreOther

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
Published2024
Admission routes2
Has abstractyes

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