Reliability of strength estimates based on limited laboratory data
Notice bibliographique
Résumé
In an open pit mine operation, the quality and quantity of collected geomechanical data can have significant implications in the design of safe and economically viable slope designs. The selection of representative values of the rock mass properties is not straightforward, given the inherent geological and structural variability within an orebody. Under these circumstances the design of a comprehensive geomechanical sampling program is critical. Such a program, however, has to comply with practical and financial constraints while developing a degree of confidence in the quality of the geomechanical data. A common target level of confidence in the rock mass properties used in slope design is higher than 80% for an open pit mine at the operations stage. This requires an ongoing maintenance of the geomechanical database and model. In practice, given the perceived high costs of laboratory testing, quite often only a relatively small number of samples are selected for laboratory testing. This leads to a series of questions pertaining to the confidence level that can be assigned to values obtained by testing only a few samples. This paper investigates the potential of small-sampling theory to provide practical recommendations on the adequacy of a testing program. The geomechanical database of an Anglo American operating open pit mine was reviewed with respect to the strength properties obtained through a series of ISRM suggested testing methods. For the purposes of this investigation the focus was on uniaxial compressive strength (UCS) results but the methodology can be applied to other material properties. In this case study, the mine geological model identified six distinct rock types. Strength values for six different rock domains were analysed using the confidence interval approach. In order to investigate the sequence of testing on the interpretation of results, statistical analyses were also performed by randomly interchanging the order of test results for each rock domain. The results showed that even if the number of specimens tested is higher than the minimum proposed by the International Society for Rock Mechanics (ISRM) suggested methods, the sample size was too small to obtain a reliable strength value for most of the rock domains. Furthermore, the results showed that the minimum sample size obtained using the confidence interval approach is significantly influenced by the test results sequence used for the analyses. Based on the results of this study, there is a demonstrated need for a method to determine the minimum sample size while minimising the influence of the testing sequence.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».