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

Abstract

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.

Cone penetration index for soil behaviour type prediction

“…Several algorithms have been proposed for the classification of soil types using probabilistic methods 37 , 76 , fuzzy logic 77 , 78 , artificial neural networks 79 – 82 , and machine learning 83 , 84 .…”

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

“…Many ML algorithms, such as gradient boosting, random forest, support vector machine (SVM) artificial neural network (ANN), and decision trees (DT), have been used in various geotechnical applications, including soil classification [27][28][29][30][31][32][33], V s prediction [23][24][25][26]34], liquefaction analysis [35][36][37][38][39][40], stability analysis [41][42][43][44][45], and settlement prediction [46][47][48]. The application of ML algorithms in geotechnical engineering has shown promising results in terms of efficiency and accuracy.…”

Learning decision boundaries for cone penetration test classification

“…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.…”