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

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 a...

Full description

Saved in:
Bibliographic Details
Published in:Environmental earth sciences 2013, Vol.68 (1), p.23-31
Main Authors: Tasdemir, Yuksel, Kolay, Ersin, Kayabali, Kamil
Format: Article
Language:English
Subjects:
Artificial neural networks
Biogeosciences
Clay
Durability
Earth and Environmental Science
Earth Sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Engineering geology
Environmental Science and Engineering
Errors
Estimating
Exact sciences and technology
Geochemistry
Geological engineering
Geology
Hydrology/Water Resources
Mathematical models
Neural networks
Original Article
Propagation
Rock
Shear strength
Statistical methods
Terrestrial Pollution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 ).
ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-012-1702-3