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A new method of modelling the rock micro-fracturing process in double-torsion experiments using neural networks
Authors:Xia-Ting Feng  Masahiro Seto
Abstract:Microfracturing of rock is a complicated damage evolution process. Inaccurate prediction of micro-fracturing behaviours suggests a need for the development of a better modelling method. Analysis of acoustic emission (AE) measurements in double-torsion tests indicates that micro-fracturing behaviours during the loading stage have fractal time structures. This fractal behaviour can be described by C(t) ∝ tD, where D is the correlation exponent, t is the time and C(t) is the correlation integral. Furthermore, by utilizing measured AE data, a new method has been developed to model the AE behaviours of micro-fracturing in rock, in air, and following soaking in water and in a chemical solution of DTAB. The neutral models NN (10,21,2) and NN (10,20,2) were found to describe reasonably well the AE behaviours of micro-fracturing in rock under air and DTAB conditions, and water conditions, respectively. The cumulative AE events and the cumulative AE counts predicted by the neural models agreed well with those measured in experiments. Copyright © 1999 John Wiley & Sons, Ltd.
Keywords:Rock microfracturing  self-similarity  fractal  neural network  double torsion  acoustic emission
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