1
Department of Civil Engineering, Babol University of Technology, Iran
2
Department of Civil Engineering, Mazandaran Institute of Technology, Iran
Abstract
Standard Penetration Test (SPT) is one of the most effective tests for quick and inexpensive evaluation of the mechanical properties of soil layers. Numerous studies have been conducted to evaluate correlations between SPT blow counts (NSPT) and the soil properties such as friction angle (). In this paper, the relation between and in situ parameters of soil including NSPT, effective stress and fine content is investigated for granular soils. In order to demonstrate the relevancy of and corrected SPT blow count (N60), a new polynomial model based on Group Method of Data Handling (GMDH) type neural networks (NN) was used based on a 195 data sets including three soil parameters. That has been recorded after two major earthquakes in Turkey and Taiwan in 1999. This study addresses the question of whether GMDH-type NN is capable to estimate based on specified variables. Results confirm that GMDH-type NN Provide an effective way to recognize data pattern and predict performance over granular soils accurately. Finally, the effect of fine content and effective overburden stress on the correlation of N60 and has been studied by sensitivity analysis.
Shooshpasha, I., Amiri, I., & MolaAbasi, H. (2015). An Investigation of Friction Angle Correlation with Geotechnical Properties for Granular Soils Using GMDH Type Neural Networks. Scientia Iranica, 22(1), 157-164.
MLA
Issa Shooshpasha; Iman Amiri; Hossein MolaAbasi. "An Investigation of Friction Angle Correlation with Geotechnical Properties for Granular Soils Using GMDH Type Neural Networks". Scientia Iranica, 22, 1, 2015, 157-164.
HARVARD
Shooshpasha, I., Amiri, I., MolaAbasi, H. (2015). 'An Investigation of Friction Angle Correlation with Geotechnical Properties for Granular Soils Using GMDH Type Neural Networks', Scientia Iranica, 22(1), pp. 157-164.
VANCOUVER
Shooshpasha, I., Amiri, I., MolaAbasi, H. An Investigation of Friction Angle Correlation with Geotechnical Properties for Granular Soils Using GMDH Type Neural Networks. Scientia Iranica, 2015; 22(1): 157-164.
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