# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
###### Abstract  
The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the cone penetration test result. Even though most present classification charts or methods were developed on the basis of data which were compiled over many countries, they should be verified to be feasible for local country. However, unfortunately, revision of those charts is quite difficult or almost impossible even though a chart provides misclassified soil class. In this research, a new method for developing soil classification model is proposed by using soft computing theory--fuzzy C-mean clustering and neuro-fuzzy theory--as a function of 5173 piezocone penetration test (PCPT) results and soil boring logs compiled from 17 local sites around Korea. Feasibility of the proposed soil classification model was verified from the viewpoint of accuracy of the classification result by comparing the classification results not only for data which were used for developing the model but also new data, which were not included in developing the model with real boring logs, other fuzzy computing classification models, and [PERSON]'s charts. The biggest advantage of the proposed method is that it is easy to make the piezocone soil classification system more accurate by updating new data. piezocone; soil classification; fuzzy C-means clustering; neuro-fuzzy +
Footnote †: journal: Applied  
relevant results. In addition, the adopted charts and methods sometimes give different soil classification results for the same input parameters, and even two charts developed by one researcher may lead to different soil types. To complement the weakness of the chart type soil classification method, and considering the fuzziness of the ground, there has been progress in studies on soft computing. [PERSON] [6] developed fuzzy membership functions on the basis of [PERSON] et al. 's chart [3], and [PERSON] and [PERSON] [7] suggested a fuzzy soil classification method according to [PERSON] and [PERSON]'s chart [2]. On the other hand, [PERSON] and [PERSON] [8] introduced a clustering method as a function of normalized cone resistance, \(Q_{t}\), and pore pressure ratio, \(B_{q}\). Clustering methods can give soil classifications between upper and lower soil data but not the soil type of each soil datum. On the other hand, soil classification by fuzzy theory can provide soil type to each soil datum and has the advantage of simply being updated for newly acquired soil data. However, it also has a problem that the classification result is highly dependent on the fuzzy membership function. As described before, [PERSON] [6] and [PERSON] and [PERSON] [7] developed fuzzy membership functions on the basis of charts such as those proposed by [PERSON] et al., and [PERSON] and [PERSON], respectively. Therefore, these fuzzy classifications seemingly remain unable to reflect local soil type. Recently, machine learning has been used to classify soils from CPT data [9; 10; 11; 12; 13] and to successfully estimate soil and design parameters [9; 13]. [PERSON] and [PERSON] [14] suggested a machine learning classifier based on a support vector machine, artificial neural network, and random forest to predict soil classes according to [PERSON] et al. [15] and soil behavior types according to [PERSON] [16; 17; 18]. They showed that machine learning algorithms can classify soils on the basis of grain size distribution and the updated soil behavior classification from [PERSON] (i.e., SBT, SBTn, ModSBTn). However, since they used cone tip resistance \(q_{c}\), sleeve friction \(f_{s}\), total vertical stress \(\sigma_{v}\), and static pore pressure \(u_{o}\) as input variables, their model can still be improved by adopting pore pressure parameters such as \(B_{q}\).