Open AccessGaussian Mixture Model Based Soil Classification Using Multiple Cone Penetration Tests
Author(s) -
Djamila Bouayad,
Julien Baroth,
Christophe Dano
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/696/1/012034
Subject(s) - mixture model , cone penetration test , expectation–maximization algorithm , multivariate statistics , cluster analysis , mathematics , mahalanobis distance , gaussian , multivariate normal distribution , bayesian probability , bayesian information criterion , statistics , probability density function , maximization , pattern recognition (psychology) , computer science , artificial intelligence , mathematical optimization , maximum likelihood , engineering , physics , geotechnical engineering , quantum mechanics
This study presents an application of the Gaussian mixture (GM) method for soil classification using multiple cone penetration tests (CPT). Compared to the hard clustering methods, the GM model classifies the CPT data by representing the probability density function of observed variables as a mixture of multivariate normal distributions. A GM model based expectation maximization (EM) algorithm with Bayesian information criterion (BIC) for selecting the optimal number of clusters is developed using six real CPT data performed at Dunkerque site in the north of France. The classification results are compared with the classical CPT based interpretation using the non normalized soil behavior type (SBT) index together with the Robertson chart. The results show that the GM model is able to identify accurately the soil layers. In addition, the combination of all CPTs, rather than considering them separately, may improve the soil layers identification because all the site information is considered.