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- W1906756274 abstract "The present paper defines a new approach to derive failure criteria for jointed rocks, using a combination of an analytical method usually followed and a coupled Artificial Neural Network (ANN). The new failure criterion is obtained by correlating the deviational stress factor of jointed rock with the uniaxial compressive stress factor of intact rock. The criterion is defined by the rock constants, A and B, which are characteristic of the joint orientation of a rock. A coupled Artificial Neural Network (ANN) system was used to predict the peak and residual axial stresses (σ1P and σ1R) at failure and the parameters of the failure criterion, given the joint orientation (s) and confining stress (σ3) at which the failure condition is required. Thus, the new failure criterion is derived using an analytical method whereas the accessibility of the constants defining the criteria is enhanced using a coupled ANN. With the new criteria in conjunction with coupled ANN model the average accuracies of prediction were 93.2%, 91.9%, 86.7% and 94.9% respectively for σ1P,σ1R, A and B." @default.
- W1906756274 created "2016-06-24" @default.
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- W1906756274 date "2002-01-01" @default.
- W1906756274 modified "2023-09-27" @default.
- W1906756274 title "A Novel Technique for the Generation of Failure Criteria for Jointed Rocks" @default.
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- W1906756274 doi "https://doi.org/10.1007/978-94-017-0081-8_53" @default.
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