What is it about?
The aim of this study is to propose a novel approach for estimating the compression index more accurately. In order to test the approach, a comparison study between AI methods has been made (multilayer neural networks, genetic programming, and multiple regression analysis). These models have been applied to samples consisting of 373 oedometer tests to predict the compression index from physical soil parameters. Based on the tangible findings, this study proposed a MATLAB program script for efficiently estimating the compression index in the future studies.
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Why is it important?
the best-fitted model proposed in these study can easily used in the future studies for estimating the compression index of a new site based on physical soil parameters, in order to help geotechnical engineers and researchers. Also, for making the use of the proposed model more easier, we proposed an algorithm programmed by MATLAB software.
Perspectives
Since the use of oedometer tests for estimating the compression index parameter is considered relatively expensive and time-consuming, I really hope that this article help engineers and researchers in the future studies.
Benbouras Mohammed Amin
Ecole Nationale Polytechnique
Read the Original
This page is a summary of: A new approach to predict the compression index using artificial intelligence methods, Marine Georesources and Geotechnology, October 2018, Taylor & Francis,
DOI: 10.1080/1064119x.2018.1484533.
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