CPTU Classification Diagrams for Identification of Sensitive Clays

Gylland, Anders Samstad; Sandven, Rolf; Montafia, Alberto; Pfaffhuber, Andreas Aspmo; Kåsin, Kristoffer; Long, Michael

doi:10.1007/978-3-319-56487-6_5

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Figs. 12 and 13 (p.453) show both datasets plotted on the chart proposed by Gylland et al (2017). It was evident that the NGTS dataset showed better agreement than the Vegvesen dataset.…”

Section: Robertson (2016)mentioning

confidence: 95%

“…To have a point with which to compare the machine learning approach, the classification was first performed using well-known charts that consider sensitive soils in their classification schemes. Charts used were those recommended by Robertson (1990Robertson ( , 2016, Eslami and Fellenius (1997), Schneider et al (2008), and Gylland et al (2017). The metric used to evaluate the accuracy was the accuracy score (A.S.), defined as follows: As this part of the work was focused on predicting the appearance of highly sensitive and quick clays from the CPTu measurements, only three soil classes were considered: sensitive, clayey, and other (coarser or stronger).…”

Section: Data Processingmentioning

confidence: 99%

“…3.5 Gylland et al (2017) This work proposes a classification chart specifically focused on the identification of sensitive clays and is based on tests performed in Norway using parameters following the same philosophy as Robertson (1990) but with a different normalization: N mc , B q1 , and R fu , as shown below:…”

Section: Robertson (2016)mentioning

confidence: 99%

See 2 more Smart Citations

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

Leiva

Depina

Thakur

2020

J. Zhejiang Univ. Sci. A

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Geotechnical classification 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, classification 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 classification 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 classification accuracy despite limited training sets.

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“…Figs. 12 and 13 (p.453) show both datasets plotted on the chart proposed by Gylland et al (2017). It was evident that the NGTS dataset showed better agreement than the Vegvesen dataset.…”

Section: Robertson (2016)mentioning

confidence: 95%

Section: Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

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

Leiva

Depina

Thakur

2020

J. Zhejiang Univ. Sci. A

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“…A practical drawback of this approach is that pore pressures are seldom measured at the cone face. To overcome it, Gylland et al (2017) proposed the use of an empirical factor, k, for the u1/u2 ratio in order to determine u1. The k value for normally consolidated clays is estimated to be around 1.25.…”

Section: Undrained Analysis Smooth Interfacementioning

confidence: 99%

Analysis of cone penetration in brittle liquefiable soils

Monforte

Gens

Arroyo

et al. 2021

Computers and Geotechnics

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“…For example, Robertson [22] proposed a few updated charts to predict soil type based on CPTu data. Gylland et al [23] used pore pressure ratio and modified cone resistance to build a set of diagrams identifying sensitive and quick clays. However, in situ tests are costly and time consuming [14], and the construction conditions are complicated.…”

Section: Introductionmentioning

confidence: 99%

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Zhu

2022

Applied Sciences

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Predicting clay sensitivity is important to geotechnical engineering design related to clay. Classification charts and field tests have been used to predict clay sensitivity. However, the imbalanced distribution of clay sensitivity is often neglected, and the predictive performance could be more accurate. The purpose of this study was to investigate the performance that extreme gradient boosting (XGboost) method had in predicting multiclass of clay sensitivity, and the ability that synthetic minority over-sampling technique (SMOTE) had in addressing imbalanced categories of clay sensitivity. Six clay parameters were used as the input parameters of XGBoost, and SMOTE was used to deal with imbalanced classes. Then, the dataset was divided using the cross-validation (CV) method. Finally, XGBoost, artificial neural network (ANN), and Naive Bayes (NB) were used to classify clay sensitivity. The F1 score, receiver operating characteristic (ROC), and area under the ROC curve (AUC) were considered as the performance indicators. The results revealed that XGBoost showed the best performance in the multiclassification prediction of clay sensitivity. The F1 score and mean AUC of XGBoost were 0.72 and 0.89, respectively. SMOTE was useful in addressing imbalanced issues, and XGBoost was an effective and reliable method of classifying clay sensitivity.

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CPTU Classification Diagrams for Identification of Sensitive Clays

Cited by 4 publications

References 6 publications

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

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

Analysis of cone penetration in brittle liquefiable soils

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

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