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

Slake durability index (Id2) 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 Id2 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 Id2. The determination coefficient (R2), 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 (Id2).

[1]  Yong Lu,et al.  Underground blast induced ground shock and its modelling using artificial neural network , 2005 .

[2]  Adnan Parlak,et al.  Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine , 2006 .

[3]  T. Singh,et al.  Slake durability study of shaly rock and its predictions , 2005 .

[4]  A. Aryafar,et al.  Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran , 2011 .

[5]  Soon Heung Chang,et al.  Radial basis function networks applied to DNBR calculation in digital core protection systems , 2003 .

[6]  T. N. Singh,et al.  Prediction of thermal conductivity of rock through physico-mechanical properties , 2007 .

[7]  O. Kisi.  The potential of different ANN techniques in evapotranspiration modelling , 2008 .

[8]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[9]  Sarath Das,et al.  Classification of slopes and prediction of factor of safety using differential evolution neural networks , 2011 .

[10]  Mahmut Firat,et al.  Comparative analysis of neural network techniques for predicting water consumption time series , 2010 .

[11]  Luis E. Vallejo,et al.  Fractal analysis of the slake durability test , 1994 .

[12]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[13]  Lyle H. Ungar,et al.  Using radial basis functions to approximate a function and its error bounds , 1992, IEEE Trans. Neural Networks.

[14]  Kunho Kim,et al.  Modelling of plasma etching using a generalized regression neural network , 2003 .

[15]  Mustafa Laman,et al.  Settlement and slaking problems in the world's fourth largest rock-fill dam, the Ataturk Dam in Turkey , 2000 .

[16]  Katsuhiko Kaneko,et al.  Slake durability and mineralogical properties of some pyroclastic and sedimentary rocks , 2002 .

[17]  Turgay PartalT. Partal.  River flow forecasting using different artificial neural network algorithms and wavelet transform , 2009 .

[18]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[19]  Paul M. Santi,et al.  Predicting the unconfined compressive strength of the Breathitt shale using slake durability, Shore hardness and rock structural properties , 1999 .

[20]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[21]  Candan Gokceoglu,et al.  Factors affecting the durability of selected weak and clay-bearing rocks from Turkey, with particular emphasis on the influence of the number of drying and wetting cycles , 2000 .

[22]  K. Kayabalı,et al.  The effect of the pH of the testing liquid on the slake durability of gypsum , 2006 .

[23]  Galip Seckin,et al.  Modeling of effluent COD in UAF reactor treating cyanide containing wastewater using artificial neural network approaches , 2010, Adv. Eng. Softw..

[24]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[25]  Chein-Lee Wang,et al.  A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan , 2009 .

[26]  Qiang Wu,et al.  Confined groundwater pollution mechanism and vulnerability assessment in oilfields, North China , 2011 .

[27]  Candan Gokceoglu,et al.  A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock , 2004, Eng. Appl. Artif. Intell..

[28]  Fikret Kocabas,et al.  Compared techniques for the critical submergence of an intake in water flow , 2010, Adv. Eng. Softw..

[29]  Yuksel Tasdemir,et al.  Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods , 2010 .

[30]  Yusuf Erzin,et al.  Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations , 2008 .

[31]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[32]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[33]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[34]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[35]  Shakeel Ahmed,et al.  Comparison of FFNN and ANFIS models for estimating groundwater level , 2011 .

[36]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[37]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

[38]  Mustafa Aytekin,et al.  Prediction of swelling pressures of expansive soils using artificial neural networks , 2010, Adv. Eng. Softw..

[39]  O. Gunaydin.  Estimation of soil compaction parameters by using statistical analyses and artificial neural networks , 2009 .

[40]  Özgür Kisi,et al.  Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel , 2010, Adv. Eng. Softw..

[41]  Osman Gencel,et al.  Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete , 2011 .

[42]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[43]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[44]  M. Rezaee,et al.  Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia , 2007 .

[45]  J. Franklin,et al.  The slake-durability test , 1972 .

[46]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[47]  Kamil Kayabali,et al.  Investigation of the effect of aggregate shape and surface roughness on the slake durability index using the fractal dimension approach , 2006 .

[48]  K. P. Sudheer,et al.  Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique , 2003 .

[49]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[50]  J. Mozejko,et al.  Application of Neural Networks for the Prediction of Total Phosphorus Concentrations in Surface Waters , 2008 .

[51]  Ahmet Tortum,et al.  Prediction of the unconfined compressive strength of compacted granular soils by using inference systems , 2009 .

[52]  Yi-Chin Fang,et al.  Neural network application for thermal image recognition of low-resolution objects , 2007 .

[53]  Işık Yilmaz,et al.  Slaking durability and its effect on the doline formation in the gypsum , 2005 .

[54]  Ali Hamza Tanrikulu,et al.  Application of ANN techniques for estimating modal damping of impact-damped flexible beams , 2009, Adv. Eng. Softw..

[55]  Donald Lawson Turcotte.  Fractals and Chaos in Geology and Geophysics: Second Edition , 1992 .