## 1 Introduction  
The methodology of a standard dilatometer test (DMT) is widely known and a detailed procedure for conducting the test was presented by [PERSON] [1] and [PERSON] et al. [2]. Until now, numerous studies have been performed to evaluate and improve some of the original correlations proposed by [PERSON] [1], but most of them have been limited to mineral soils. Comprehensive investigations have been made to assess and expand the application of DMT in geotechnical engineering [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. In most cases, the relationships used to evaluate geotechnical parameters from the dilatometer test such as the corrected first reading \(p_{o}\) or corrected second reading \(p_{1}\), or index parameters such as the material index \(I_{D}\), horizontal stress index \(K_{D}\), and dilatometer modulus \(E_{D}\), are commonly applied.  
Undrained shear strength is the basic parameter in the geotechnical design of different structures. Determination of this parameter using dilatometer tests is usually based on empirical formulas. However, it should be noted that most of these formulas have been established in different countries; therefore, regional geotechnical conditions could have substantially affected the empirical relationships and the values of empirical coefficients.  
In geotechnical engineering, Artificial Neural Networks (ANNs) have been used to analyse many issues, as reviewed by: [PERSON] [18], [PERSON] et al. [19], [PERSON] et al. [20; 21], [PERSON] et al. [22], and [PERSON] [23]. The use of ANNs for the prediction of the lateral earth pressure ratio K\({}_{o}\) based on a dilatometer test and undrained shear strength of soft clays was presented by [PERSON] and [PERSON] [24] and [PERSON] et al. [25], respectively. However, Artificial Neural Networks have not yet been applied to evaluate the undrained shear strength of organic soils from dilatometer tests.  
This paper presents the results of field and laboratory tests of organic soils such as peat (amorphous and pseudo-fibrous), calcareous soil called \"gyttja\" (calcarous and calcareous-organic), mud, and organic mud. The undrained shear strength of organic soils can be evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical correlations presented in the literature. However, the empirical methods may sometimes show relatively high values of maximum relative error. Therefore, a method for evaluating the undrained shear strength of organic soils using artificial neural networks based on data obtained from a dilatometer test and organic soil properties is presented in this study. The presented neural network, with an architecture of 5-4-1, predicts the normalized undrained shear strength based on five independent variables: the normalized net value of a corrected first pressure reading (\(p_{o}-u_{o}\))/\(\sigma^{\prime}v\), the normalized net value of a corrected second pressure reading (\(p_{1}-u_{o}\))/\(\sigma^{\prime}v\), the organic content \(I_{om}\), the void ratio \(e\), and the stress history indictor (\(\infty\) or \(nc\)). The neural model presented in this study provided a more reliable prediction of the undrained shear strength in comparison to the empirical methods, with a maximum relative error of \(\pm\)10%.