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Bulletin of Engineering Geology and the Environment
Published in: Bulletin of Engineering Geology and the Environment 2/2010

01-05-2010 | Original Paper

Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods

Authors: Ersin Kolay, Kamil Kayabali, Yuksel Tasdemir

Published in: Bulletin of Engineering Geology and the Environment | Issue 2/2010

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Abstract

Clay bearing, weathered and other weak rocks cause major problems in engineering practice due to their interactions with water. The slake durability index (I d2) is an important tool used to assess the resistance of these rocks to erosion and degradation, but sample preparation for this test is tedious. The paper reports an attempt to define I d2 through statistical models using other parameters that are simpler to obtain. The main objective of this study was to define the best empirical relationship between the I d2 and the point load strength index (I s(50)), dry unit weight (γ d) and fractal dimension (D) parameters of eight rock types by applying general multiple linear regression (GLM), artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The models obtained were evaluated using the R 2, MSE, MARE and d parameters. The results indicate that the relationships between I d2 and γ d, I s(50) and D were best obtained using ANN, followed by GLM and ANFIS. It is concluded that ANN modelling is a fast and practical method of establishing I d2.
previous article Hydro-mechanical features of landslide reactivation in weak clayey rock masses
next article Effect of nonlinearity on site response and ground motion due to earthquake excitation

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Metadata
Title
Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods
Authors
Ersin Kolay
Kamil Kayabali
Yuksel Tasdemir
Publication date
01-05-2010
Publisher
Springer-Verlag
Published in
Bulletin of Engineering Geology and the Environment / Issue 2/2010
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI
https://doi.org/10.1007/s10064-009-0259-1

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