Methodology for the design of dynamic rock supports in burst prone ground
Notice bibliographique
Résumé
The depth at which underground mines operate has been increasing continuously which is particularly true in the case of hard rock mining. The stability issues associated with mining at great depth pose tough challenges to engineers and researchers alike. Long-term mine developments in deep hard rock mines such as haulage drifts need to be functional during the entire life of the mine plan without posing any major stability concerns, which will otherwise hamper the production and other logistics associated with mining operations. High convergence and rockburst hazards are the main problems due to high stress and mining-induced seismicity in deep hard rock mining. In such circumstances, the understanding of drift support behavior under static and dynamic conditions is crucial for mining engineers when dealing with drift stability in deep, hard rock mines. In this thesis, current design methods for selecting drift support systems are reviewed, which are mostly dependent on empirical approaches and are geared towards static support design. Based on this, the current research focuses on ground support analysis under both static and dynamic conditions to understand drift support behavior with respect to nearby mining. Numerical modeling of drift primary and secondary supports is performed by developing two models using the 2-dimensional FLAC code. Axial loads induced in the drift support system under static and dynamic conditions are estimated for the case study hard rock mine in Canada at a depth of 1500 m. The results of numerical modeling are obtained in terms of axial loads in the drift support system, wall damage due to tension under dynamic conditions, and the extent of rock mass yielding around the drift. It is found that mining on the same level is critical to drift stability under static conditions, and rock mass yielding in the south wall of the drift (towards the ore body) extends beyond the bolting horizon once this stage begins. The results also show that by providing secondary support before same level mining commences, drift stability is greatly enhanced. The static model is calibrated through the implementation of an in-situ monitoring program of axial loads induced at the head of the rockbolt. A new load monitoring device called U-cell is successfully used for this purpose. Measured and estimated axial loads are then compared and found to be in good agreement. The preliminary dynamic analysis shows that a peak particle velocity of 2.0 m/s at the periphery of the drift will cause wall damage more than 1.0 m when only primary supports are provided, and around 0.5 m when secondary supports are installed along with the primary ones, and when there is no nearby mining taking place. The effects of lower level and same level mining under dynamic conditions are also examined, and wall damage and rock mass yielding are estimated. The estimation of wall damage depth is crucial in designing dynamic rock supports. It is demonstrated that wall damage due to various levels of ground motion can be estimated by dynamic numerical modeling. Finally, a methodology for the design of dynamic rock supports is presented, which is based on the selection of yielding support type and pattern, the estimation of the ejection velocity, and the volume of wall damage as obtained from dynamic modeling.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».