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What is Unsupervised Stratigraphic Analysis of CPT about?

This article presents a Bayesian machine learning approach for unsupervised and simultaneous soil stratigraphic interpretation of cone penetration test (CPT) soundings at a site scale. The method leverages statistical similarities and spatial correlations from multiple CPT soundings to enhance the accuracy and consistency of stratigraphic interpretations, addressing limitations of traditional separated interpretation methods. The proposed approach has been validated with real-world data, demonstrating signi

Author

Huỳnh Văn Thịnh

Language

EN