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International Conference on Artificial Neural Networks

ICANN 2010: Artificial Neural Networks – ICANN 2010 pp 42–45Cite as

Prediction of Compaction Characteristics of Granular Soils by Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6352))

Abstract

New experimental data discussed in [5] are used in the present paper. Application of the penalized error function, Principle Data Analysis and Bayesian criterion of Maximum Marginal Likelihood enabled design and training of numerically efficient small neural networks. They were applied for identification of two compaction characteristics, i.e. Optimum Water Content and Maximum Dry Density of granular soils.

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References

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Author information

Authors and Affiliations

  1. Chair of Structural Mechanics, Rzeszów University of Technology, ul. W. Pola 2, 35-959, Rzeszów, Poland

    Marzena Kłos & Zenon Waszczyszyn

Authors
  1. Marzena Kłos
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  2. Zenon Waszczyszyn
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Editor information

Editors and Affiliations

  1. Department of Informatics, TEI of Thessaloniki, 57400, Sindos, Greece

    Konstantinos Diamantaras

  2. School of Physics, Astronomy, and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Wlodek Duch

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200, Orestiada, Thrace, Greece

    Lazaros S. Iliadis

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© 2010 Springer-Verlag Berlin Heidelberg

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Kłos, M., Waszczyszyn, Z. (2010). Prediction of Compaction Characteristics of Granular Soils by Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-15819-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15818-6

  • Online ISBN: 978-3-642-15819-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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