Comparer des méthodes

	Critère de Rupture de Hoek-Brown	Algorithme de Lerchs-Grossmann	Classification RMR (Rock Mass Rating)
Domaine	Génie minier	Génie minier	Génie minier
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1980	1965	1973
Auteur d'origine	Evert Hoek and E. T. Brown	Helmut Lerchs and Israel Grossmann	Zbigniew T. Bieniawski
Type	Empirical criterion for rock mass strength prediction	Graph-theoretic algorithm for pit limit optimization	Empirical classification for geotechnical engineering
Source fondatrice	Hoek, 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.	Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54.	Bieniawski, Z. T. (1989). Engineering rock mass classifications. John Wiley & Sons. ISBN: 978-0-471-60437-4
Alias	Generalized Hoek-Brown Criterion, HB Criterion	Lerchs-Grossmann Method, LG Algorithm	RMR, Bieniawski Classification, RMR89
Apparentées	3	4	3
Résumé	The 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.