NDLI: Estimation of strength parameters of rock using artificial neural networks

Content Provider	Springer Nature Link
Author	Sarkar, Kripamoy Tiwary, Avyaktanand Singh, T. N.
Copyright Year	2010
Abstract	The accurate determination of geomechanical properties such as uniaxial compressive strength and shear strength requires considerable time in collecting appropriate samples, their preparation and laboratory testing. To minimize the time and cost, a number of empirical relations have been reported which are widely used for the estimation of complex rock properties from more easily acquired data. This paper reports the use of an artificial neural network to predict the deformation properties of Coal Measure rocks using dynamic wave velocity, point load index, slake durability index and density. The results confirm the applicability of this method.La détermination précise des propriétés géomécaniques telles que la résistance à la compression simple et la résistance au cisaillement demande beaucoup de temps pour le choix des échantillons, leur préparation et la réalisation des essais de laboratoire. Afin de minimiser le temps et le coût, plusieurs relations empiriques ont été présentées, largement utilisées pour l’estimation des propriétés des roches à partir de données plus facilement obtenues. L’article présente l’utilisation d’un réseau de neurones artificiel destiné à prévoir les propriétés de déformation de roches d’une série houillère à partir de mesures de vitesses des ondes, de l’indice de compression entre pointes, l’indice de durabilité et la densité. Les résultats confirment l’applicabilité de cette méthode.
Starting Page	599
Ending Page	606
Page Count	8
File Format	PDF
ISSN	14359529
Journal	Bulletin of the International Association of Engineering Geology - Bulletin de l'Association Internationale de Géologie de l'Ingénieur
Volume Number	69
Issue Number	4
e-ISSN	14359537
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-07-02
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Réseaux de neurones artificiels Propriétés des roches Paramètres de résistance Geoecology/Natural Processes Nature Conservation Geoengineering, Foundations, Hydraulics Applied Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Geology Geotechnical Engineering and Engineering Geology

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