Soil classification based on unsupervised learning using cone penetration test data

Abstract

Abstract Borehole data obtained during geological surveys are the most essential source for understanding soil stratification. It is a prerequisite to know the soil classes up to some depths prior to any construction. However, the direct method to identify the soil classes by drilling boreholes and testing soil samples is costly. A cost-effective alternative is the Cone Penetration Testing (CPT), which is one of the most popular soil investigation methods. This paper explores the intelligent classification of soil layers based on CPT data using three unsupervised machine learning methods: K-means, Gaussian Mixture Process, and BIRCH. The research investigates the classification performance of different models in scenarios with 2 combinations, 3 combinations, 4 combinations, and 5 combinations. The results indicate that the Gaussian Mixture Process method exhibits the best classification performance, followed by the BIRCH method, while K-means performs relatively poorly. Using unsupervised learning for intelligent soil layer classification offers a fast and clear process, but the accuracy still requires further improvement. This study provides a valuable reference for future soil classification studies.