基于机器学习的CPT土类划分研究

    Study on Soil Classification Based on CPT Data Using Machine Learning Techniques

    • 摘要: 近年来,国内外学者基于CPT数据建立了不少土质分类的经验公式和模型。由于地域、标准、目的等各异,在分类模型与区分的土类方面,不同学者的结论不尽相同。本文以我国南海莺歌海盆地60个钻孔CPT数据和与之毗邻的钻孔取样为研究对象,首先,结合邻近位置的钻孔,精选了5类常见土(黏土、粉质黏土、粉土、粉砂及细砂)对应的CPT数据,并进行了解释性处理;其次,以归一化摩阻比和归一化锥尖阻力为分类指标,采用多种机器学习方法(包括随机森林、人工神经网络、决策树、贝叶斯学习、支持向量机和线性感知器等)得到了适用于我国南海莺歌海盆地的土体行为分类模型;然后,采用混淆矩阵、总体精度和Kappa系数等指标对各分类模型的分类准确性进行了评估;最后,将得到的模型应用于研究区域周边及较远海域的CPT自动分类中,并用与CPT邻近的钻孔取样对其进行检验。结果表明,本文得到的土体行为分类模型对我国南海北部海域CPT数据分类具有较高的准确率。

       

      Abstract: Many empirical formulas and models for soil classification based on Cone Penetration Test (CPT) data have been developed. However, the conclusions from different scholars vary in terms of the classification model of soils and the distinguishment of soil types due to the differences in geographical locations, standards and research objectives. In this study, the CPT data of 60 boreholes obtained in the Yinggehai Basin in the South China Sea and the samples taken from the adjacent boreholes are studied. Firstly, based on the data of boreholes located nearby, the CPT data corresponding to five common types of soils (i.e. clay, silty clay, silt, silty sand and fine sand) are selected and then processed interpretively. Secondly, soil behavior classification models suitable for using in the Yinggehai Basin are built up by using the normalized frictional ratio and the normalized cone tip resistance as the classification indicators and applying various machine learning techniques including Random Forest, Artificial Neural Networks, Decision Trees, Bayesian Learning, Support Vector Machines and Linear Perceptions. Thirdly, the metrics such as confusion matrix, overall accuracy and Kappa coefficient are used to evaluate the classification accuracy of the models. Finally, the developed models are applied to automatically interpret CPT data in the surrounding and distant sea areas, and then validated using borehole samples adjacent to the CPT data. The results indicate that the soil behavior classification models developed in this study demonstrate high accuracies in interpreting CPT data in the northern part of the South China Sea, providing valuable insights for geotechnical engineering applications in offshore wind energy projects and other marine construction activities.

       

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