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01.12.2020 | Technical paper

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

verfasst von: Mohammed el Amin Bourouis, Abdeldjalil Zadjaoui, Abdelkader Djedid

Erschienen in: Innovative Infrastructure Solutions | Ausgabe 3/2020

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Abstract

Fine soils have the particularity of producing very slow settlement over time, particularly secondary settlement, also known as creep. The coefficient C_α 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.

Vorheriger Artikel Possible effects and reactions between leachate and different clay material types

Nächster Artikel Utilization of industrial waste—based geopolymers as a soil stabilizer—a review

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Titel: Contribution of two artificial intelligence techniques in predicting the secondary compression index of fine-grained soils
verfasst von: Mohammed el Amin Bourouis
Abdeldjalil Zadjaoui
Abdelkader Djedid
Publikationsdatum: 01.12.2020
Verlag: Springer International Publishing
Erschienen in: Innovative Infrastructure Solutions / Ausgabe 3/2020
Print ISSN: 2364-4176
Elektronische ISSN: 2364-4184
DOI: https://doi.org/10.1007/s41062-020-00348-1

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