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Learning decision boundaries for cone penetration test classification
Author(s) -
Erharter Georg H.,
Oberhollenzer Simon,
Fankhauser Anna,
Marte Roman,
Marcher Thomas
Publication year - 2021
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12662
Subject(s) - cone penetration test , computer science , artificial intelligence , penetration test , machine learning , artificial neural network , geotechnical engineering , geology , subgrade
In geotechnical field investigations, cone penetration tests (CPT) are increasingly used for ground characterization of fine‐grained soils. Test results are different parameters that are typically visualized in CPT based data interpretation charts. In this paper we propose a novel methodology which is based on supervised machine learning that permits a redefinition of the boundaries within these charts to account for unique soil conditions. We train ensembles of randomly generated artificial neural networks to classify six soil types based on a database of hundreds of CPT tests from Austria and Norway. After training we combine the multiple unique solutions for this classification problem and visualize the new decision boundaries in between the soil types. The generated boundaries between soil types are comprehensible and are a step towards automatically adjusted CPT interpretation charts for specific local conditions.