Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

Applied Sciences

MariaJolanta Sulewska¹  Simon Rabarijoely²  Zbigniew Lechowicz²  Masaharu Fukue³ 

[1] Faculty of Civil and Environmental Engineering, Bialystok University of Technology, Wiejska 45E St., 15-351 Bialystok, Poland;Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences–SGGW, Nowoursynowska 159 St., 02-776 Warsaw, Poland;Tokai University, 3-20-1, Orido Shimizu-ku, Shizuoka 424-8610, Japan;

关键词: organic soils;    undrained shear strength;    dilatometer test;    artificial neural networks;   

DOI  :  10.3390/app8081395

来源: DOAJ

【 摘 要 】

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 (po − uo)/σ′v, the normalized net value of a corrected second pressure reading (p1 − uo)/σ′v, the organic content Iom, the void ratio e, and the stress history indictor (oc 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 ±10%.

【 授权许可】

Unknown