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CLC number: TU19

On-line Access: 2020-06-10

Received: 2019-10-29

Revision Accepted: 2020-04-26

Crosschecked: 2020-05-14

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Cristian Godoy

https://orcid.org/0000-0001-8449-982X

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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.445-461

http://doi.org/10.1631/jzus.A1900556


Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests


Author(s):  Cristian Godoy, Ivan Depina, Vikas Thakur

Affiliation(s):  Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim 7031, Norway; more

Corresponding email(s):   cristg@stud.ntnu.no

Key Words:  Machine learning, Classifi, cation, Quick clays, Sensitive clays


Cristian Godoy, Ivan Depina, Vikas Thakur. Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests[J]. Journal of Zhejiang University Science A, 2020, 21(6): 445-461.

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Abstract: 
Geotechnical classifi;cation is vital for site characterization and geotechnical design. Field tests such as the cone penetration test with pore water pressure measurement (CPTu) are widespread because they represent a faster and cheaper alternative for sample recovery and testing. However, classifi;cation schemes based on CPTu measurements are fairly generic because they represent a wide variety of soil conditions and, occasionally, they may fail when used in special soil types like sensitive or quick clays. Quick and highly sensitive clay soils in Norway have unique conditions that make them difficult to be identified through general classifi;cation charts. Therefore, new approaches to address this task are required. The following study applies machine learning methods such as logistic regression, Naive Bayes, and hidden Markov models to classify quick and highly sensitive clays at two sites in Norway based on normalized CPTu measurements. Results showed a considerable increase in the classifi;cation accuracy despite limited training sets.

应用机器学习方法从静力触探结果中识别快黏土和高灵敏度黏土

目的:研究机器学习技术在利用孔压静力触探测试(CPTu)识别高灵敏度黏土和快黏土的潜力.
创新点:1. 成功应用机器学习方法从CPTu结果中分类出高灵敏度黏土和快黏土,并将结果与不同地点的实际土层进行了比较. 2. 通过对机器学习算法的多次训练确定了可以获得良好结果的最少CPTu个数.
方法:1. 基于对两个位置已知和土层确定的CPTu数据集的分析,使用3种机器学习图像分类方法(逻辑回归、朴素贝叶斯和隐藏马尔科夫模型)将CPTu数据用于样本分类. 2. 将结果与实际土层进行比较,识别高灵敏度黏土和快黏土,并从计算性能度量方面比较3个方法的优缺点.
结论:仅采用4个CPTu训练样本便可获得基于逻辑回归、朴素贝叶斯和隐藏马尔科夫模型的识别高灵敏度黏土和快黏土的3个分类模型,且分类精度良好.

关键词:机器学习; 分类; 快黏土; 高灵敏度黏土

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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