Neural identification of compaction characteristics for granular soils

  • Marzena Kłos Chair of Structural Mechanics, Rzeszow University of Technology, Rzeszów
  • Zenon Waszczyszyn Chair of Structural Mechanics, Rzeszow University of Technology, Rzeszów
  • Maria Sulewska Faculty of Civil and Environmental Engineering, Bialystok University of Technology, Białystok

Abstract

The paper is a continuation of [9], where new experimental data were analysed. The Multi-Layered Perceptron and Semi-Bayesian Neural Networks were used. The Bayesian methods were applied in Semi-Bayesian NNs to the design and learning of the networks. Advantages of the application of the Principal Component Analysis are also discussed. Two compaction characteristics, i.e. Optimum Water Content and Maximum Dry Density of granular soils were identified. Moreover, two different networks with two and single outputs, corresponding to the compaction characteristics, are analysed.

Keywords

granular soils, compaction characteristics, Optimum Water Content (OWC), Maximum Dry Density (MDD), neural networks, Multi-Layered Perceptron (MLP), Semi-Bayesian NN (SBNN), Principal Component Analysis (PCA),

References

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Published
Jan 25, 2017
How to Cite
KŁOS, Marzena; WASZCZYSZYN, Zenon; SULEWSKA, Maria. Neural identification of compaction characteristics for granular soils. Computer Assisted Methods in Engineering and Science, [S.l.], v. 18, n. 4, p. 265–273, jan. 2017. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/104>. Date accessed: 31 may 2025.
Citation Formats
Issue
Vol 18 No 4 (2011)
Section
Articles