CLC number: TU19
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-05-14
Cited: 0
Clicked: 3265
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.
@article{title="Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests",
author="Cristian Godoy, Ivan Depina, Vikas Thakur",
journal="Journal of Zhejiang University Science A",
volume="21",
number="6",
pages="445-461",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900556"
}
%0 Journal Article
%T Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests
%A Cristian Godoy
%A Ivan Depina
%A Vikas Thakur
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 6
%P 445-461
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900556
TY - JOUR
T1 - Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests
A1 - Cristian Godoy
A1 - Ivan Depina
A1 - Vikas Thakur
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 6
SP - 445
EP - 461
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1900556
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.
[1]Eslami A, Fellenius BH, 1997. Pile capacity by direct CPT and CPTu methods applied to 102 case histories. Canadian Geotechnical Journal, 34(6):886-904.
[2]Gylland AS, Sandven R, Montafia A, et al., 2017. CPTU classification diagrams for identification of sensitive clays. In: Thakur V, L’Heureux JS, Locat A (Eds.), Landslides in Sensitive Clays. Springer, Cham, Switzerland, p.57-66.
[3]hmmlearn, 2010. hmmlearn: Unsupervised Learning and Inference of Hidden Markov Models. hmmlearn. https://hmmlearn.readthedocs.io
[4]L’Heureux J, Lindgård A, Emdal A, 2019. The Tiller-Flotten Research Site: Geotechnical Characterization of a Very Sensitive Clay Deposit. Technical Report No. 20160154-20-R. Norwegian Geotechnical Institute, Norway.
[5]Lunne T, Robertson PK, Powell JJM, 1997. Cone Penetration Testing in Geotechnical Practice. Blackie Academic and Professional, London, UK.
[6]NGI (Norwegian Geotechnical Institute), 2019. NGTS-Norwegian Geo-test Sites. NGI. https://www.ngi.no/eng/Projects/NGTS-Norwegian-Geo-Test-Sites
[7]Pedregosa F, Varoquaux G, Gramfort A, et al., 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
[8]Robertson PK, 1990. Soil classification using the cone penetration test. Canadian Geotechnical Journal, 27(1):151-158.
[9]Robertson PK, 2016. Cone penetration test (CPT)-based soil behaviour type (SBT) classification system–an update. Canadian Geotechnical Journal, 53(12):1910-1927.
[10]Schneider JA, Randolph MF, Mayne PW, et al., 2008. Analysis of factors influencing soil classification using normalized piezocone tip resistance and pore pressure parameters. Journal of Geotechnical and Geoenvironmental Engineering, 134(11):1569-1586.
[11]Scikit-learn, 2019. Scikit-learn User Guide. Release 0.21.2. Scikit-learn. https://scikit-learn.org/dev/versions.html
[12]Statens Vegvesen, 2013. County Road 715 Keiserås-Olsøy, Parcel: Leksvik Border-Olsøy. Data Report Nr. 2012039995-009/Ud925Ar09. Rissa, Norway (in Norwegian).
[13]Valsson SM, 2016. Detecting quick clay with CPTu. Proceedings of the 17th Nordic Geotechnical Meeting: Challenges in Nordic Geotechnics.
[14]van Rossum G, 1995. Python Reference Manual. Technical Report No. CS-R9525. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.
[15]Wickremesinghe DS, 1989. Statistical Characterization of Soil Profiles Using in Situ Tests. PhD Thesis, University of British Columbia, Vancouver, Canada.
Open peer comments: Debate/Discuss/Question/Opinion
<1>