| contributor author | Pijush Samui | |
| contributor author | T. G. Sitharam | |
| contributor author | Pradeep U. Kurup | |
| date accessioned | 2017-05-08T21:29:15Z | |
| date available | 2017-05-08T21:29:15Z | |
| date copyright | June 2008 | |
| date issued | 2008 | |
| identifier other | %28asce%291090-0241%282008%29134%3A6%28894%29.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/53363 | |
| description abstract | The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance | |
| publisher | American Society of Civil Engineers | |
| title | OCR Prediction Using Support Vector Machine Based on Piezocone Data | |
| type | Journal Paper | |
| journal volume | 134 | |
| journal issue | 6 | |
| journal title | Journal of Geotechnical and Geoenvironmental Engineering | |
| identifier doi | 10.1061/(ASCE)1090-0241(2008)134:6(894) | |
| tree | Journal of Geotechnical and Geoenvironmental Engineering:;2008:;Volume ( 134 ):;issue: 006 | |
| contenttype | Fulltext | |
