中南大学学报（自然科学版）2023，Vol.54Issue(11)：4391-4402,12.DOI:10.11817/j.issn.1672-7207.2023.11.017
基于随机森林算法的CPTu土类识别模型研究及其在不同区域的应用
Research on CPTu-based soil classification model using random forest algorithm and its application in different regions
摘要
Abstract
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.关键词
CPTu/土类识别/随机森林/泛化性能/不平衡分类Key words
CPTu/soil classification/random forest/generalization performance/imbalance classification分类
建筑与水利引用本文复制引用
伍圣超,王睿,张建民..基于随机森林算法的CPTu土类识别模型研究及其在不同区域的应用[J].中南大学学报（自然科学版）,2023,54(11):4391-4402,12.基金项目
国家自然科学基金资助项目(52022046,52038005)(Projects(52022046,52038005)supported by the National Natural Science Foundation of China) （52022046,52038005）
