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Hoek-Browns kriterium for brud×Lerchs-Grossmann Algoritmen×Rock Mass Rating (RMR)×
FagområdeMinedriftMinedriftMinedrift
FamilieProcess / pipelineProcess / pipelineProcess / pipeline
Oprindelsesår198019651973
OphavspersonEvert Hoek and E. T. BrownHelmut Lerchs and Israel GrossmannZbigniew T. Bieniawski
TypeEmpirical criterion for rock mass strength predictionGraph-theoretic algorithm for pit limit optimizationEmpirical classification for geotechnical engineering
Oprindelig kildeHoek, E., & Brown, E. T. (2002). The Hoek-Brown failure criterion and GSI: 2018 update. Journal of Rock Mechanics and Geotechnical Engineering, 10(2), 445-463. link ↗Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54. link ↗Bieniawski, Z. T. (1989). Engineering rock mass classifications. John Wiley & Sons. ISBN: 978-0-471-60437-4
AliasserGeneralized Hoek-Brown Criterion, HB CriterionLerchs-Grossmann Method, LG AlgorithmRMR, Bieniawski Classification, RMR89
Relaterede343
ResuméThe Hoek-Brown Criterion, developed by Evert Hoek and E. T. Brown starting in 1980, is an empirical failure criterion that predicts the shear strength of rock masses as a function of confining pressure. It accounts for rock quality (via the Geological Strength Index, GSI) and thus bridges laboratory rock mechanics and field behavior. The criterion is widely used in mining for slope stability, pillar design, and stress analysis.The Lerchs-Grossmann Algorithm is a graph-theoretic method for determining the ultimate pit limit in open-pit mining operations. Introduced by Helmut Lerchs and Israel Grossmann in 1965, it maximizes the net present value of extracted ore while respecting slope stability constraints. This algorithm forms the theoretical foundation for most modern pit optimization software.The Rock Mass Rating (RMR) system, developed by Zbigniew Bieniawski starting in 1973, is an empirical classification that characterizes rock mass quality and estimates mining and civil engineering behavior. RMR combines five measurable geotechnical parameters into a single index ranging from 0 to 100, where higher values indicate stronger, more stable rock masses. It is the most widely used rock classification system worldwide for underground mining design.
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ScholarGateSammenlign metoder: Hoek-Brown Criterion · Lerchs-Grossmann Algorithm · Rock Mass Rating. Hentet 2026-06-19 fra https://scholargate.app/da/compare