利用机器学习进行基于锥入度测试的土壤分类

M. Fatehnia, V. Mahmoudabadi, Sharid Amiri
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引用次数: 0

摘要

锥入度试验(CPT)在岩土工程中被广泛用于评估土壤性质。解读 CPT 数据和对土壤进行分类的传统方法具有局限性,而且耗时较长。机器学习(ML)算法提供了一种数据驱动的方法,可根据 CPT 数据自动改进土壤分类。本研究调查了 ML 技术的适用性,以衡量使用原始 CPT 数据进行土壤分类预测的可靠性。数据集包括原始 CPT 数据和从相邻钻孔获得的相应土壤分类,用于训练和测试所选的 ML 技术。应用了五种 ML 算法,即逻辑回归、支持向量机、随机森林 (RF)、K-近邻 (KNN) 和极梯度提升 (XGBoost)。结果表明,RF 算法的性能优于其他 ML 方法,F1 分数达到 0.896。在比较不同算法的性能时,RF 始终显示出最佳结果,其次是 XGBoost 和 KNN。这些发现凸显了 ML 算法(尤其是 RF)在基于 CPT 数据准确预测土壤分类方面的潜力,从而提高了岩土工程应用的效率和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing Machine Learning for Cone Penetration Test-Based Soil Classification
The cone penetration test (CPT) is widely used in geotechnical engineering to assess soil properties. Traditional methods of interpreting CPT data and classifying soils have limitations and are time-consuming. Machine learning (ML) algorithms offer a data-driven approach to automate and improve soil classification based on CPT data. In this study, the applicability of ML techniques was investigated to measure the reliability of soil classification prediction using raw CPT data. A dataset comprising raw CPT data and corresponding soil classifications derived from the adjacent boreholes was prepared for training and testing the selected ML techniques. Five ML algorithms, namely logistic regression, the support vector machine, the random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), were applied. The results showed that the RF algorithm outperformed other ML methods, achieving an F1-score of 0.896. Comparing the performance of different algorithms, the RF consistently showed the best results, followed by XGBoost and KNN. These findings highlight the potential of ML algorithms, particularly the RF, in accurately predicting soil classification based on CPT data, thus improving the efficiency and reliability of geotechnical engineering applications.
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