基于CPTU数据融合技术的黏性土应力历史与强度特性评价研究

刘晓燕; 蔡国军; 邹海峰; 李学鹏; 刘松玉

doi:10.11779/CJGE201907011

基于CPTU数据融合技术的黏性土应力历史与强度特性评价研究 English Version

东南大学岩土工程研究所,江苏南京 211189

基金项目: 国家重点研发计划项目课题（2016YFC0800200）; 国家自然科学基金项目（41672294,41877231,51578146）

详细信息

作者简介:
刘晓燕(1990— ),女,博士研究生,主要从事土工原位测试与能源岩土工程方面的研究工作。E-mail: happyliuxiaoyan@163.com。

通讯作者:
蔡国军，E-mail：focuscai@163.com

中图分类号: TU413
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出版历程
- 收稿日期: 2018-05-03
- 发布日期: 2019-07-24

Prediction of stress history and strength of cohesive soils based on CPTU and data fusion techniques

Institute of Geotechnical Engineering, Southeast University, Nanjing 211189, China

摘要

摘要: 超固结比（OCR）和不排水抗剪强度（S_u）是土的基本力学参数,对土体沉降变形分析和稳定性计算具有重要影响。采用数据融合技术,结合孔压静力触探（CPTU）测试数据,提出了江苏典型黏性土超固结比和不排水抗剪强度的预测模型。利用特征级数据融合技术（回归树、模型树）与决策级数据融合技术（自举聚合、堆叠泛化）对预测模型的可行性进行分析。将土的超固结比和不排水抗剪强度的预测值、室内试验所得到的参考值以及CPTU传统方法所得到的估计值进行对比分析。结果表明,模型树预测结果比回归树要好,决策级融合算法可以提高回归树的预测结果,但对模型树的预测结果影响较小;叠加回归树和模型树的预测结果会使其预测的不排水抗剪强度比回归树预测的结果要好,但比模型树预测的结果要差;对于几种数据融合模型,OCR预测值大致相当,回归树模型在预测OCR方面稍优于其他数据融合模型,数据融合技术能更好地预测土的超固结比和不排水抗剪强度。
- 孔压静力触探 /
- 数据融合技术 /
- 超固结比 /
- 不排水抗剪强度 /
- 黏性土
Abstract: The overconsolidation ratio (OCR) and the undrained shear strength (S_u) are the basic mechanical parameters of soils, which can influence the deformation analysis and strength calculation of soils. A prediction model for OCR and S_uof typical clay in Jiangsu Province is proposed by using the data fusion technique and the data of piezocone penetration test (CPTU). The feasibility of the prediction model is analyzed by using the feature-level data fusion techniques (regression tree, model tree) and decision-level data fusion techniques (bagging, stacking). The predicted OCR and S_u, the reference values obtained by the laboratory tests and the estimated values obtained by the existing calculation methods are compared and analyzed. The results show that the predicted results of the model tree are better than those of the regression tree. The decision and fusion algorithms can improve the predicted results of the regression tree, but they have little influences on the predicted results of the model tree. The superimposed regression tree and model tree can make the predicted S_u better than that of the regression tree, but worse than that of the model tree. For several data fusion models, the predicted OCR is approximately close. The regression tree model is slightly better than other data fusion models in predicting the OCR. Compared with other prediction methods, the data fusion model can better predict the OCR and S_u.
- CPTU /
- data fusion technique /
- OCR /
- undrained shear strength /
- clay

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参考文献(21)

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