Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks

Laboratory prediction of the unconfined compression strength (UCS) of cohesive soils is important to determine the shear strength properties. However, this study presents the application of different methods simple-multiple analysis and artificial neural networks for the prediction of the UCS from basic soil properties. Regression analysis and artificial neural networks prediction indicated that there exist acceptable correlations between soil properties and unconfined compression strength. Besides, artificial neural networks showed a higher performance than traditional statistical models for predicting UCS. Regression analysis and artificial neural network prediction indicated strong correlations (R^2=0.71-0.97) between basic soil properties and UCS. It has been shown that the correlation equations obtained by regression analyses are found to be reliable in practical situations.

[1]  Howard B. Demuth,et al.  Neutral network toolbox for use with Matlab , 1995 .

[2]  R. Corotis.  Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp. , 1988 .

[3]  M. Budhu,et al.  Non-linear analysis of laterally loaded piles in heavily overconsolidated clays , 1986 .

[4]  Yingjie Yang,et al.  The application of neural networks to rock engineering systems (RES) , 1998 .

[5]  L. Bjerrum,et al.  Embankments on Soft Ground , 1973 .

[6]  Zhu Ruigeng,et al.  An engineering geology evaluation method based on an artificial neural network and its application , 1997 .

[7]  Doug Schmucker,et al.  Not As Bad As It Seems: Teaching Probability And Statistics In Civil Engineering , 2004 .

[8]  Julián M. Londoño,et al.  On the applicability of neural networks for soil dynamic amplification analysis , 2001 .

[9]  F. Mosteller,et al.  Data Analysis and Regression , 1978 .

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

[11]  A. Skempton,et al.  A Contribution to the settlement analysis of foundations on clay , 1957 .

[12]  W. D. Kovacs,et al.  An Introduction to Geotechnical Engineering , 1981 .

[13]  Nabil Kallas,et al.  Modeling soil collapse by artificial neural networks , 2004 .

[14]  Prabir Kumar Basudhar,et al.  Prediction of residual friction angle of clays using artificial neural network , 2008 .

[15]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[16]  Christer Sjöström,et al.  State-of-the-art report , 1997 .

[17]  J. A. Ware,et al.  Using neural networks to predict workability of concrete incorporating metakaolin and fly ash , 2003 .

[18]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[19]  Sunil K. Sinha,et al.  Artificial Neural Network Prediction Models for Soil Compaction and Permeability , 2008 .

[20]  Renato Lancellotta,et al.  NEW DEVELOPMENTS IN FIELD AND LABORATORY TESTING OF SOILS. PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON SOIL MECHANICS AND FOUNDATION ENGINEERING, SAN FRANCISCO, 12-16 AUGUST 1985 , 1985 .

[21]  A. Ersoy,et al.  Specific Energy Prediction for Circular Diamond Saw in Cutting Different Types of Rocks Using Multivariable Linear Regression Analysis , 2005 .

[22]  Laverne W. Stanton,et al.  Applied Regression Analysis: A Research Tool , 1990 .

[23]  Harry G. Poulos,et al.  Stress deformation and strength characteristics , 1977 .

[24]  U. Atici,et al.  Prediction of the strength of mineral-addition concrete using regression analysis , 2010 .

[25]  Mauro Serra,et al.  Concrete strength prediction by means of neural network , 1997 .

[26]  Seung-Rae Lee,et al.  An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation , 2003 .

[27]  T. Taskiran,et al.  Prediction of California bearing ratio (CBR) of fine grained soils by AI methods , 2010, Adv. Eng. Softw..

[28]  Frederick Mosteller,et al.  Data Analysis and Regression , 1978 .

[29]  S. Kahraman,et al.  Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks , 2009, Expert Syst. Appl..

[30]  K. Terzaghi,et al.  Soil mechanics in engineering practice , 1948 .

[31]  D. M. Ellis,et al.  Applied Regression Analysis , 1968 .

[32]  In Mo Lee,et al.  Prediction of pile bearing capacity using artificial neural networks , 1996 .

[33]  R. H. Myers.  Classical and modern regression with applications , 1986 .

[34]  Harry G. Poulos,et al.  Stress - deformation and strength characteristics: state of the art report , 1977 .