z-logo
open-access-imgOpen Access
Correlation Analysis between Soil Shear Strength Parameters and Cone Index Using Artificial Neural Networks - 1
Author(s) -
In-Jong Moon,
Young-Uk Kim
Publication year - 2015
Publication title -
journal of the korea academia-industrial cooperation society
Language(s) - English
Resource type - Journals
eISSN - 2288-4688
pISSN - 1975-4701
DOI - 10.5762/kais.2015.16.3.2234
Subject(s) - artificial neural network , shear strength (soil) , geotechnical engineering , shear (geology) , mathematics , soil water , materials science , geology , computer science , artificial intelligence , soil science , composite material
This study has been undertaken to develop a relationship between the shear strength coefficients and the cone index. The theoretic mathematical equations for the relationship were rigorously investigated, and then a Artificial Neural Network(ANN) analysis was adapted to enhance the reliability of the investigation. The theoretical investigation involved various assumptions resulting in the significant error involvement of geotechnical behaviors of ground. Therefore, a model using the ANN has been learned to enhance the prediction of the cone index form the shear strength parameters. Site investigation reports from various construction fields were used for ANN model learning. The results of the study show that the model predicts the cone index from the shear strength parameters of soils very well. The further study that is undertaking has a potential promise of the generalized prediction technique for the cone index from the soil parameters.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom