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Prediction of Critical Hydraulic Gradient of Coir Fibre Mixed Soil using Artificial Neural Network


Prediction of Critical Hydraulic Gradient of Coir Fibre Mixed Soil using Artificial Neural Network
Authors : Arya S S, Dr. Usha Thomas
Publication Date: 04-03-2017

Authors

Author(s):  Arya S S, Dr. Usha Thomas

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 03 , March - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS030029

Abstract

Water retaining structures are of great importance now a day because of the scarcity of water. In order to avoid the mixing of salt water from lake into the fresh river water some temporary water retaining structures are constructed across the river during summer season in some regions. The stability of these structures is thus very important. One of the main causes of failure of water retaining structure is piping failure. Here, in this study the possibility of using natural fibres for reducing the seepage velocity and piping failure of plain laterite soil is investigated. Seepage flow of water is in the upward direction and it induces reduction in effective stress and finally piping failure to soil. The ability of soil to resist piping failure is termed as piping resistance of soil. Thus here the study is conducted to know how the ability of soil to resist piping failure is improved, while natural fibre such as coir fibre is mixed randomly with soil. For quantifying the piping resistance of coir fibre mixed soil, critical hydraulic gradient is found out experimentally. Critical hydraulic gradient is the hydraulic gradient at piping failure. Different soil samples from Thiruvananthapuram city are examined. Along with experimental method an Artificial Neural Network is used for predicting the critical hydraulic gradient. Artificial Neural Network is modeled using MATLAB software.

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