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.

中文翻译：

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学习锥渗透测试分类的决策边界

在岩土工程领域的调查中，越来越多地使用圆锥渗透试验（CPT）来表征细粒土壤。测试结果是通常在基于CPT的数据解释表中可视化的不同参数。在本文中，我们提出了一种基于监督机器学习的新颖方法，该方法允许重新定义这些图表中的边界以说明独特的土壤条件。我们根据来自奥地利和挪威的数百种CPT测试的数据库，训练随机生成的人工神经网络的集成，以对六种土壤类型进行分类。经过培训，我们结合了针对该分类问题的多种独特解决方案，并可视化了土壤类型之间的新决策边界。