Identification of Soil Strata Based on General Regression Neural Network Model From CPTU Data

References

• Abu-Farsakh , M. Y. , Z. J. Zhang , M. T. Tumay , and M. Morvant . 2008 . Computerized cone penetration test for soil classification-development of MS-Windows software. Journal of Transportation Research Board, TRR No. 2053: 47–64.
   Google Scholar
• ASTM. 2006. Standard practice for classification of soils for engineering purposes (unified soil classification system). D2487–06. ASTM Book of Standards. West Conshohocken, PA: Author.
   Google Scholar
• Cai , G. , S. Liu , and L. Tong . 2010 . Field evaluation of deformation characteristics of a lacustrine clay deposit using seismic piezocone tests. Engineering Geology 116 ( 3–4 ): 251 – 260 .
   Web of Science ®Google Scholar
• Cai , G. , S. Liu , and A. J. Puppala . 2011a . Evaluation of pile bearing capacity from piezocone penetration test data in soft Jiangsu Quaternary clay deposits . Marine Georesources & Geotechnology 29 ( 3 ): 177 – 201 .
   Web of Science ®Google Scholar
• Cai , G. , S. Liu , and A. J. Puppala . 2011b . Comparison of CPT charts for soil classification using PCPT data: Example from clay deposits in Jiangsu Province, China. Engineering Geology 121 ( 1–2 ): 89 – 96 .
   Web of Science ®Google Scholar
• Cetin , K. O. and N. Isik . 2007 . Probabilistic assessment of stress normalization for CPT data . Journal of Geotechnical and Geoenvironmental Engineering 133 ( 7 ): 887 – 897 .
   Web of Science ®Google Scholar
• Cetin , K. O. and C. Ozan . 2009 . CPT-based probabilistic soil characterization and classification . Journal of Geotechnical and Geoenvironmental Engineering 135 ( 1 ): 84 – 107 .
   Web of Science ®Google Scholar
• Douglas , J. B. and R. S. Olsen . 1981 . Soil classification using electric cone penetrometer. Proceedings of the Conference on Cone Penetration Testing and Experience, American Society of Civil Engineers, St. Louis, MO, 209–227.
   Google Scholar
• Eslami , A. and B. H. Fellenius . 1997 . Pile capacity by direct CPT and PCPT methods applied to 102 case histories . Canadian Geotechnical Journal 34 ( 6 ): 880 – 898 .
   Google Scholar
• Goh , A. T. C. 1999 . Soil laboratory data interpretation using generalized regression neural network . Civil Engineering and Environmental Systems 16 ( 3 ): 175 – 195 .
   Web of Science ®Google Scholar
• Hegazy , Y. A. 1998 . Delineating geostratigraphy by cluster analysis of piezocone data[D]. Civil and Environmental Engineering Department, Georgia Institute of Technology , Atlanta , GA .
   Google Scholar
• Hegazy , Y. A. and P. W. Mayne . 2002 . Objective site characterization using clustering of piezocone data . Journal of Geotechnical and Geoenvironmental Engineering 128 ( 12 ): 986 – 996 .
   Web of Science ®Google Scholar
• Jefferies , M. G. and M. P. Davies . 1991 . Soil classification using the cone penetration test (Discussion) . Canadian Geotechnical Journal 28 ( 1 ): 173 – 176 .
   Web of Science ®Google Scholar
• Jefferies , M. G. and M. P. Davies . 1993 . Use of PCPT to estimate equivalent SPT N60 . Geotechnical Testing Journal 16 ( 4 ): 458 – 468 .
   Google Scholar
• Jung , B. C. , P. Gardoni , and G. Biscontin . 2008 . Probabilistic soil identification based on cone penetration tests . Geotechnique 58 ( 7 ): 591 – 603 .
   Web of Science ®Google Scholar
• Juang , C. H. , C. J. Chen , and Y. M. Tien . 1999 . Appraising CPT-based liquefaction resistance evaluation method–artificial neural network approach . Canadian Geotechnical Journal 36 : 443 – 454 .
   Web of Science ®Google Scholar
• Juang , C. H. , T. Jiang , and R. A. Christopher . 2001 . Three dimensional site characterization: Neural network approach . Geotechnique 51 ( 9 ): 799 – 809 .
   Web of Science ®Google Scholar
• Juang , C. H. and P. C. Lu . 2002 . Predicting geotechnical parameters of sands from CPT measurements using neural networks . Computer-Aided Civil and Infrastructure Engineering 17 ( 1 ): 31 – 42 .
   Web of Science ®Google Scholar
• Kurup , P. U. and N. K. Dudani . 2002 . Neural networks for profiling stress history of clays from PCPT data . Journal of Geotechnical and Geoenvironmental Engineering 128 ( 7 ): 569 – 579 .
   Web of Science ®Google Scholar
• Kurup , P. U. and E. P. Griffin . 2006 . Prediction of soil composition from CPT data using general regression neural network . Journal of Computing in Civil Engineering 20 ( 4 ): 281 – 289 .
   Web of Science ®Google Scholar
• Liu , S. , G. Cai , L. Tong , and G. Du . 2008 . Approach on the engineering properties of Lianyungang marine clay from piezocone penetration tests . Marine Georesources & Geotechnology 26 ( 3 ): 189 – 210 .
   Web of Science ®Google Scholar
• Lunne , T. , P. K. Robertson , and J. J. M. Powell . 1997 . Cone Penetration Testing in Geotechnical Practice . London : Blackie Academic and Professional, Chapman & Hall .
   Google Scholar
• Meng , G. , S. Liu , and S. Ma . 2000 . Application research of piezocone penetration test (CPTU) in pearl river delta . Geological Science and Technology Information 19 ( 1 ): 81 – 85 .
   Google Scholar
• Olsen , R. S. and P. G Malone . 1988 . Soil classification and site characterization using the cone penetrometer test. Penetration testing 1988. Proceedings of the First International Symposium on Penetration Testing ISOPT-1 , De Ruiter , J. (ed.), Rotterdam , The Netherlands : A. A. Balkema , 887 – 893 .
   Google Scholar
• Patterson , D. W. 1996 . Artificial Neural Networks: Theory and Applications. Singapore : Prentice-Hall .
   Google Scholar
• Penumadu , D. and R. Zhao . 1999 . Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) . Journal of Enhanced Heat Transfer 24 ( 3 ): 207 – 230 .
   Google Scholar
• Pradhan , T. B. S. 1998 . Soil identification using piezocone data by fuzzy method . Soils and Foundations 38 ( 1 ): 255 – 262 .
   Google Scholar
• Robertson , P. K. , R. G. Campanella , D. Gillespie , and J. Greig . 1986 . Use of piezometer cone data. Proceedings of the ASCE Specialty Conference on In Situ'86: Use of In Situ Tests in Geotechnical Engineering, Blacksburg, VA, 1263–1280.
   Google Scholar
• Robertson , P. K. 1990 . Soil classification using the cone penetration test . Canadian Geotechnical Journal 27 ( 1 ): 151 – 158 .
   Web of Science ®Google Scholar
• Robertson , P. K. and C. E. Wride . 1998 . Evaluating cyclic liquefaction potential using the cone penetration test . Canadian Geotechnical Journal 35 ( 3 ): 442 – 459 .
   Web of Science ®Google Scholar
• Robertson , P. K. 2009 . Interpretation of cone penetration tests—a unified approach . Canadian Geotechnical Journal 46 ( 9 ): 1337 – 1355 .
   Google Scholar
• Schneider , J. A. , M. F. Randolph , P. W. Mayne , and N. R. Ramsey . 2008. Analysis of factors influencing soil classification using normalized piezocone tipresistance and pore pressure parameters. Journal of Geotechnical and Geoenvironmental Engineering 134(11): 1569–1586.
   Web of Science ®Google Scholar
• Senneset , K. and N. Janbu . 1985 . Shear strength parameters obtained from static cone penetration tests. Proceedings of the Strength Testing of Marine Sediments; Laboratory and In situ Measurement, ASTM Special Technical Publication, STP 883, 41–54.
   Google Scholar
• Shahin , M. A. , M. B. Jaksa , and H. R. Maier . 2001 . Artificial neural network applications in geotechnical engineering . Australian Geomechanics 36 ( 1 ): 49 – 62 .
   Google Scholar
• Specht , D. F. 1991 . A general regression neural network. IEEE Trans . Neural Network 2 ( 6 ): 568 – 576 .
   Web of Science ®Google Scholar
• Wroth , C. P. 1984 . Interpretation of in-situ soil test . Geotechnique 34 ( 4 ): 449 – 489 .
   Web of Science ®Google Scholar
• Zhang , Z. and M. T. Tumay . 1996 . Simplification of soil classification charts derived from cone penetration test . Geotechnical Testing Journal 19 ( 2 ): 203 – 216 .
   Web of Science ®Google Scholar
• Zhang , Z. and M. T. Tumay . 1999 . Statistical to fuzzy approach toward CPT soil classification . Journal of Geotechnical and Geoenvironmental Engineering 125 ( 3 ): 79 – 186 .
   Google Scholar
• Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/umgt.
   Google Scholar