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Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data ( N c )
Author(s) -
Samui Pijush,
Sitharam T. G.
Publication year - 2010
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.837
Subject(s) - support vector machine , relevance vector machine , algorithm , mathematics , overburden , engineering , artificial intelligence , computer science , geotechnical engineering
Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values ( N ) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value ( N c ), N values have been corrected ( N c ) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N c = N c ( X, Y, Z ), where X , Y and Z are the coordinates of a point corresponding to N c value, is to be approximated in which N c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.