Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

doi:10.1371/journal.pone.0176656

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…In comparison to other methods of geotechnical investigation, CPTs are generally well suited for ML as they are (currently) one of the few geotechnical tests that aim at high resolution data acquisition and generate high quality and high quantity data. There have been several applications of supervised and unsupervised ML for different CPT‐related tasks (e.g., Goh, 1995; Kohestani, Hassanlourad, & Ardakani, 2015; Rogiers et al., 2017). See, for example, Carvalho and Ribeiro (2019) who use the two ML classification algorithms K‐nearest neighbors and distance weighted nearest neighbors to replicate the CPT classifications according to Robertson (2009, 2016) and provide a comprehensive list of papers related to CPT and ML.…”

Section: Introductionmentioning

confidence: 99%

Learning decision boundaries for cone penetration test classification

Erharter

Oberhollenzer

Fankhauser

et al. 2021

Computer aided Civil Eng

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In geotechnical field investigations, cone penetration tests (CPT) are increasingly used for ground characterization of fine‐grained soils. Test results are different parameters that are typically visualized in CPT based data interpretation charts. In this paper we propose a novel methodology which is based on supervised machine learning that permits a redefinition of the boundaries within these charts to account for unique soil conditions. We train ensembles of randomly generated artificial neural networks to classify six soil types based on a database of hundreds of CPT tests from Austria and Norway. After training we combine the multiple unique solutions for this classification problem and visualize the new decision boundaries in between the soil types. The generated boundaries between soil types are comprehensible and are a step towards automatically adjusted CPT interpretation charts for specific local conditions.

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Section: Introductionmentioning

confidence: 99%

Learning decision boundaries for cone penetration test classification

Erharter

Oberhollenzer

Fankhauser

et al. 2021

Computer aided Civil Eng

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show abstract

“…Most work that use machine learning techniques for classifying soil from CPT data apply clustering to propose new soil classes (Hegazy & Mayne, 2002;Facciorusso & Uzielli, 2004;Bhattacharya & Solomtine, 2006;Liao & Mayne, 2007;Das & Basudhar, 2009;Rogiers et al, 2017;Wang et al, 2019). One limitation of these work is the reduced number of input features included, most times only two.…”

Section: Introductionmentioning

confidence: 99%

“…Another limitation is that most work explore only hierarchical clustering techniques (Hegazy & Mayne, 2002;Facciorusso & Uzielli, 2004;Bhattacharya & Solomtine, 2006;Liao & Mayne, 2007). Nevertheless, a recent study stated that including depth as an input can improve cluster-ing results and that the x-means algorithm can lead to good results (Rogiers et al, 2017). In spite of these conclusions, to the best knowledge of the authors, no work from the literature investigated clustering techniques including all measured CPT parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of kernel k-means and kernel x-means clustering to obtain soil classes from cone penetration test data

Carvalho¹,

Ribeiro²

2020

S&R

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Most methods available in the literature for soil classification from cone penetration test (CPT) data define soil classes using laboratory tests. One disadvantage of this approach is that field soil conditions are difficult to replicate in a lab. The alternative adopted in this work is trying to define soil classes only by the similarity of the CPT measurements, using clustering. This study is the first, to the best knowledge of the authors, to cluster soil classes in a four-dimensional input feature space using measurements directly taken from the CPT experiment. Nine soil classes are produced from a general dataset containing 179 CPT soundings and, in a complementary study, four more specialized classes are obtained from 5 CPT soundings. Artificial neural networks (ANN) are used to produce simple models capable of reproducing both class groups, which are compared with classical soil classifications from the literature and with standard penetration test (SPT) samples. Results show that both general and specialized class groups can be reproduced by ANN although accuracy is better for the latter, reaching a 97.04 % accuracy with a standard deviation of 1.24 %. Furthermore, it is shown that accuracies above 80 % are obtained even if incomplete data is used. This shows that the here proposed soil classes can become an interesting alternative in engineering practice.

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“…For soil classification systems there are two main approaches, one is replicating existing soil classification systems and the other is trying to propose new ones. Most work in this research field are dedicated to the latter approach, using data clustering (Hegazy & Mayne, 2002;Facciorusso & Uzielli, 2004;Liao & Mayne, 2007;Das & Basudhar, 2009;Rogiers et al, 2017). Usually, among the few work that investigate replicating existing soil classification systems such as Robertson charts (Arel, 2012), the only ML technique tested is ANN (Kurup & Griffin, 2006;Reale et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Soil Classification System from Cone Penetration Test Data Applying Distance-Based Machine Learning Algorithms

Carvalho¹,

Ribeiro²

2019

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Most work from the literature dedicated to soil classification systems from cone penetration test (CPT) data are based on simple two-dimensional charts. One alternative approach is using machine learning (ML) to produce new soil classification systems or to reproduce existing ones. The available studies within this research field can be considered limited, once most of them do not include more than two inputs within their analysis and are applicable only to specific regions. In this context, the aim of this work is to use distance-based ML techniques to replicate two chart-based methods from the literature. Up to five input feature combinations are tested, with the objective of discussing geotechnical aspects of soil classification systems. Results are compared using the statistical test of Friedman with the post-hoc statistics of Nemenyi and the signed-rank statistical test of Wilcoxon. The used dataset can be considered diversified because it contains 111 CPT soundings from several countries. Results show that the used ML techniques maintain reasonable accuracy when inputs are substituted and when incomplete data is used, which can lead to cost reduction in real engineering projects. It is important to notice that these observations would not be possible by using the replicated soil classification systems alone.

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Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

Cited by 14 publications

References 38 publications

Learning decision boundaries for cone penetration test classification

Learning decision boundaries for cone penetration test classification

Application of kernel k-means and kernel x-means clustering to obtain soil classes from cone penetration test data

Soil Classification System from Cone Penetration Test Data Applying Distance-Based Machine Learning Algorithms

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