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Enregistrement W3176615821 · doi:10.82308/37055

Geotechnical risk assessment of mine haulage drifts during the life of a mine plan

2013· article· en· W3176615821 sur OpenAlexfundaboutno aff
Wael Rashad Elrawy Abdellah

Notice bibliographique

RevueeScholarship@McGill (McGill) · 2013
Typearticle
Langueen
DomaineEngineering
ThématiqueMining Techniques and Economics
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésHaulageMining engineeringGeologyPlan (archaeology)Geotechnical engineeringGeotechnical investigationUnderground mining (soft rock)Coal miningEngineeringWaste management

Résumé

récupéré en direct d'OpenAlex

Mine developments such as haulage drifts and cross-cuts are the primary access to the mining blocks of an orebody in multilevel mining systems for tabular ore deposits. Thus, their stability is of utmost importance during the planned period of production or the life of a mine plan. Many Canadian underground mines use longitudinal and transverse stoping with delayed backfill to extract tabular ore deposits. These methods require access to the orebody through a number of sill drives or cross-cuts which link the orezone to the haulage drift hence creating intersections on multiple levels. Mine development instability could lead to serious consequences such as injuries, production delays and higher operational cost. The objective of this research is to develop a hybrid approach in which deterministic numerical modelling is integrated with probabilistic methods to evaluate the stability of mine developments due to nearby mining activity. A case study comprising four consecutive mine levels in a deep underground metal mine in Sudbury, Ontario has been adopted for this study. The stability performance of the haulage drift is assessed using two separate evaluation criteria, namely Mohr-Coulomb yield function and Brittle Shear Failure. Random Monte-Carlo (RMC) technique is then employed in conjunction with Finite difference modelling software FLAC to determine the probability of instability or unsatisfactory performance of the haulage drift with respect to nearby mining sequence. In this study, the haulage drift performance is considered unsatisfactory when the yield zones or brittle shear failure around the haulage drift extend beyond the anchorage limit of the rock support. A comparison of the results from Mohr-Coulomb and Brittle Shear conditions has revealed that Mohr-Coulomb is more conservative from a design point of view. A three-dimensional, elastoplastic, finite difference model (FLAC 3D) is then constructed to simulate the case study mining orezone. The unsatisfactory performance of the intersection is evaluated with respect to mining sequence in terms of the strength-to-stress ratio computed by FLAC3D. Unsatisfactory stability performance is defined by a strength-to-stress ratio that is less than 1.4 and its corresponding extent into the rockmass around the intersection. Due to the large size of the FLAC3D model, the probabilistic simulations are conducted with the Point-Estimate Method (PEM), which requires significantly lesser number of simulations than Random Monte-Carlo (RMC). The results are presented and categorized with respect to probability, instability, and mining stage. In order to validate the numerical model, Multi-point borehole extensometers (MPBX) are installed at selected intersections to monitor the rock deformations as mining activities progress. The monitoring results revealed a lateral shift of the drift walls toward the orebody and much less deformations in the drift back. Finally, a methodology is developed to estimate the geotechnical risk of drift instability by considering the probability of failure and cost of consequence of such failure at an intersection. A 5-level risk index is derived which ranges from low to extreme. The methodology is demonstrated through an intersection from the case study mine, and the risk index is shown to vary with mining sequence. It is shown that the risk-index methodology can be used to confirm the need for enhanced supports, but it can also be used as basis for the comparison alternative mine designs.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,233
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,211
Écart entre enseignants0,198 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2013
Routes d'admission2
Résumé présentoui

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