CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences

Stefan Rauter¹  Franz Tschuchnigg¹ 

[1] Institute of Soil Mechanics, Foundation Engineering and Computational Geotechnics, Graz University of Technology, 8010 Graz, Austria;

关键词: cone penetration test;    soil classification;    machine learning;    artificial neural network;    support vector machine;    random forest;   

DOI  :  10.3390/geosciences11070265

来源: DOAJ

【 摘 要 】

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance q_c, sleeve friction f_s, friction ratio R_f, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σ_v, effective vertical stresses σ’_v and hydrostatic pore pressure u₀), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

【 授权许可】

Unknown