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Estimation of probabilistic CPT-based soil profile using an unsupervised Gaussian mixture model by Ressol R. Shakir; Hui Wang is a Earth and Planetary Sciences article available to read on EtoBox.

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This paper develops a Gaussian Mixture Model (GMM) method to produce the soil profile based on the cone penetration test results (CPT). The theoretical probabilistic model utilizes the Bayesian information criterion (BIC) and Akaike information criteria (AIC) to estimate the number of layers. The research analyzed the impact of four types of covariance matrices on soil classification. The proposed approach was applied to the real-life CPT data for the National Geotechnical Experimentation Site (NGES) at Texas, which was widely used for analysis and comparison. Evaluation of the proposed model with many previous classification systems indicated that the GMM could detect soil boundaries and types by clustering the data and employing Robertson chart. In addition to that, the clustering decision depended on the posterior probability of every soil unit to the corresponding clusters. The thickness of the thin layers and the location of boundaries rely on the type of covariance matrix. The study revealed that the four types of covariance give a range of layers from 5 to 7, corresponding to the minimum values of both BIC and AIC. The nonshared full covariance matrix reflected more layer bo

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Author

Ressol R. Shakir; Hui Wang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)