Research on CPTu-based soil classification model using random forest algorithm and its application in different regions

The feasibility of building a multi-regional soil classification model based on the cross-regional "CPTu+ borehole" database was investigated, and it illustrates that a single soil classification model can be suitable for multiple regions with four major classifications: gravel, sand, silt and clay. A "CPTu & borehole" database from New Zealand, Austria and Germany was established, and a soil classification machine learning model was developed based on random forest algorithm using the eight statistical characteristics of CPTu data as inputs and four soil classes, including gravel, sand, silt, and clay, as outputs. Furtherly, the performance of four kinds of machine learning algorithms, namely RF, SVM, BPANN and KNN, were discussed in detail for CPTu-based soil classification. The results show that the soil classification model has good generalization performance in three regions, i.e., New Zealand, Austria and Germany, and exhibits remarkable better performance than SBTn method. Combined with an appropriate soil boundary determination method, the model can successfully reconstruct the soil stratification at the CPTu testing point. The reconstructed soil stratification has good consistency with corresponding borehole results, and the consistency level is about 95%. The RF algorithm shows optimal performance for solving this imbalance classification problem. © 2023 Central South University of Technology. All rights reserved.