Prediction of consistency parameters of fen soils by neural networks
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2014, Vol 21, Issue 1
Abstract
This paper presents application of artificial neural networks (ANNs) for prediction of consistency parameters (plastic limit, liquid limit) of fen soils in comparison with the standard regression analysis. All samples of cohesive soils were retrieved from the Wisłok river floodplain, in the vicinity of Rzeszów, near Lisia Góra (Fox Mountain) reserve. Basic fractions (clay, silt, sand) of fen soils are independent variables in modeling of soil properties. Two regression models and a standard multi-layer back-propagation net have been used.
Authors and Affiliations
Artur Borowiec, Krzysztof Wilk
Keywords
Related Articles
- EP ID EP73587
- DOI -
- Views 162
- Downloads 0
Modelling of concrete cover degradation caused by rebar?s corrosion- multicomponent media theory approach. (Received in the final form January 23, 2010)
This paper presents the model based on the theory of multicomponent media that allows modelling of rebar corrosion processes. The presented model extends and consolidates the dissertations that have been described in the...
Analysis of fluid-structure interaction of a torus subjected to wind loads
In the paper the aerodynamic forces acting on a part of a water slide or other object with curved, tubular shape, depending on the section of a torus and value of the wind velocity, were obtained. This was done by means...
Multiobjective evolutionary optimization of MEMS structures. (Received in the final form November 12, 2010)
The paper is devoted to the shape optimization of piezoelectric and electro-thermo-mechanical devices by the use of multiobjective evolutionary algorithm. In this paper, special implementation of multiobjective evolution...
Multibody approach in suspension system optimization
In this paper an approach of optimization of suspension system parameters is described. Taking into consideration the stiffness and damping coefficients of springs and shock absorbers of a heavy road transport vehicle se...
Neural identification of compaction characteristics for granular soils
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 desig...