Authors: Yuksel Tasdemir Ersin Kolay Kamil Kayabali
Publish Date: 2012/08/11
Volume: 68, Issue: 1, Pages: 23-31
Abstract
Slake durability index I d2 is an important engineering parameter to assess the resistance of claybearing and weak rocks to erosion and degradation Standard test sample preparation for slake durability test is difficult for some rock types and the test is timeconsuming The paper reports an attempt to define I d2 using other parameters that are simpler to obtain In this study three different artificial neural network approaches namely feedforward back propagation FFBP radial basis function based neural network RBNN and generalized regression neural networks GRNN were used for estimating I d2 The determination coefficient R 2 root mean square error and mean absolute relative error statistics were used as evaluation criteria of the FFBP RBNN and GRNN models The experimental results were compared with these models The comparison results indicate that the GRNN models are superior to the FFBP and RBNN models in modeling of the slake durability index I d2
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