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Contribution of two artificial intelligence techniques in predicting the secondary compression index of fine-grained soils

Contribution of two artificial intelligence techniques in predicting the secondary compression... Fine soils have the particularity of producing very slow settlement over time, particularly secondary settlement, also known as creep. The coefficient Cα that characterizes the creep phenomenon seems difficult to evaluate in the laboratory and in situ. Two approaches are proposed in this article for a better and faster prediction of that coefficient. The first approach is based on machine learning using multi-gene genetic programming, and the second one uses hybridization of particle swarm optimization algorithms and artificial neural networks. A regression analysis allowed identifying the determinant parameters to be used in the calculations. A database from several sites, and containing 203 samples, was utilized. The findings showed that a good agreement exists between the predicted and measured values. This also indicates that these two techniques can be quite interesting for engineers when they have to design works on compressible soils. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Innovative Infrastructure Solutions Springer Journals

Contribution of two artificial intelligence techniques in predicting the secondary compression index of fine-grained soils

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Publisher
Springer Journals
Copyright
Copyright © Springer Nature Switzerland AG 2020
ISSN
2364-4176
eISSN
2364-4184
DOI
10.1007/s41062-020-00348-1
Publisher site
See Article on Publisher Site

Abstract

Fine soils have the particularity of producing very slow settlement over time, particularly secondary settlement, also known as creep. The coefficient Cα that characterizes the creep phenomenon seems difficult to evaluate in the laboratory and in situ. Two approaches are proposed in this article for a better and faster prediction of that coefficient. The first approach is based on machine learning using multi-gene genetic programming, and the second one uses hybridization of particle swarm optimization algorithms and artificial neural networks. A regression analysis allowed identifying the determinant parameters to be used in the calculations. A database from several sites, and containing 203 samples, was utilized. The findings showed that a good agreement exists between the predicted and measured values. This also indicates that these two techniques can be quite interesting for engineers when they have to design works on compressible soils.

Journal

Innovative Infrastructure SolutionsSpringer Journals

Published: Aug 11, 2020

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