Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.

[1]  W. C. Krumbein,et al.  Permeability as a Function of the Size Parameters of Unconsolidated Sand , 1943 .

[2]  J. Kozeny.  Das Wasser im Boden. Grundwasserbewegung , 1953 .

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

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

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  K. M. Onuoha,et al.  New statistical grain-size method for evaluating the hydraulic conductivity of sandy aquifers , 1989 .

[8]  Russell G. Shepherd,et al.  Correlations of Permeability and Grain Size , 1989 .

[9]  H. J. Morel-Seytoux,et al.  Unsaturated flow in hydrologic modeling. Theory and practice. , 1989 .

[10]  W. Rawls,et al.  Estimation of Soil Water Retention and Hydraulic Properties , 1989 .

[11]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[12]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. I , 1990, IEEE Trans. Syst. Man Cybern..

[13]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[14]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[15]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[16]  J. D. Jabro,et al.  Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data , 1992 .

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[19]  Zekâi Şen,et al.  Determination of Hydraulic Conductivity from Complete Grain‐Size Distribution Curves , 1993 .

[20]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[21]  J. Jeffrey Peirce,et al.  A Model for Estimating the Hydraulic Conductivity of Granular Material Based on Grain Shape, Grain Size, and Porosity , 1995 .

[22]  Paolo Frasconi,et al.  Learning without local minima in radial basis function networks , 1995, IEEE Trans. Neural Networks.

[23]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[24]  Robert Babuška,et al.  Fuzzy model for the prediction of unconfined compressive strength of rock samples , 1999 .

[25]  Cornelius T. Leondes,et al.  Fuzzy Theory Systems: Techniques and Applications , 1999 .

[26]  Yaochu Jin,et al.  Techniques in Neural-Network-Based Fuzzy System Identification and Their Application to Control of Complex Systems , 1999 .

[27]  Marcel G. Schaap,et al.  Saturated hydraulic conductivity prediction from microscopic pore geometry measurements and neural network analysis , 1999 .

[28]  Y. L. Loukas,et al.  Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. , 2001, Journal of medicinal chemistry.

[29]  Yike Guo,et al.  A rule based fuzzy model for the prediction of petrophysical rock parameters , 2001 .

[30]  Alaa F. Sheta,et al.  Time-series forecasting using GA-tuned radial basis functions , 2001, Inf. Sci..

[31]  Loon Ching Tang,et al.  Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..

[32]  Giles M. Foody,et al.  Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes , 2004 .

[33]  Juan Julián Merelo Guervós,et al.  Evolving RBF neural networks for time-series forecasting with EvRBF , 2004, Inf. Sci..

[34]  M. Gribb,et al.  Hydraulic Conductivity Prediction for Sandy Soils , 2004, Ground water.

[35]  Haralambos Sarimveis,et al.  A classification technique based on radial basis function neural networks , 2006, Adv. Eng. Softw..

[36]  Christian W. Dawson,et al.  The effect of different basis functions on a radial basis function network for time series prediction: A comparative study , 2006, Neurocomputing.

[37]  I. Yilmaz.  Indirect estimation of the swelling percent and a new classification of soils depending on liquid limit and cation exchange capacity , 2006 .

[38]  Paramasivan Saratchandran,et al.  Improved GAP-RBF network for classification problems , 2007, Neurocomputing.

[39]  Kin Keung Lai,et al.  Multistage RBF neural network ensemble learning for exchange rates forecasting , 2008, Neurocomputing.

[40]  I. Yılmaz,et al.  An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters , 2008 .

[41]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

[42]  I. Yilmaz,et al.  Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models , 2009 .

[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]  Işık Yilmaz,et al.  The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks , 2010 .

[45]  Işık Yilmaz,et al.  Forecasting of natural gas consumption with neural network and neuro fuzzy system , 2010 .