Utilizing Machine Learning for Cone Penetration Test-Based Soil Classification

Abstract

The cone penetration test (CPT) is widely used in geotechnical engineering to assess soil properties. Traditional methods of interpreting CPT data and classifying soils have limitations and are time-consuming. Machine learning (ML) algorithms offer a data-driven approach to automate and improve soil classification based on CPT data. In this study, the applicability of ML techniques was investigated to measure the reliability of soil classification prediction using raw CPT data. A dataset comprising raw CPT data and corresponding soil classifications derived from the adjacent boreholes was prepared for training and testing the selected ML techniques. Five ML algorithms, namely logistic regression, the support vector machine, the random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), were applied. The results showed that the RF algorithm outperformed other ML methods, achieving an F1-score of 0.896. Comparing the performance of different algorithms, the RF consistently showed the best results, followed by XGBoost and KNN. These findings highlight the potential of ML algorithms, particularly the RF, in accurately predicting soil classification based on CPT data, thus improving the efficiency and reliability of geotechnical engineering applications.