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Environmental Earth Sciences

Erschienen in:

01.01.2013 | Original Article

Comparison of three artificial neural network approaches for estimating of slake durability index

verfasst von: Yuksel Tasdemir, Ersin Kolay, Kamil Kayabali

Erschienen in: Environmental Earth Sciences | Ausgabe 1/2013

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Abstract

Slake durability index (I _d2) is an important engineering parameter to assess the resistance of clay-bearing 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 time-consuming. 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 feed-forward 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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Titel: Comparison of three artificial neural network approaches for estimating of slake durability index
verfasst von: Yuksel Tasdemir
Ersin Kolay
Kamil Kayabali
Publikationsdatum: 01.01.2013
Verlag: Springer-Verlag
Erschienen in: Environmental Earth Sciences / Ausgabe 1/2013
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-012-1702-3

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