Neural identification of compaction characteristics for granular soils
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2011, Vol 18, Issue 4
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.
Authors and Affiliations
Marzena Kłos, Zenon Waszczyszyn, Maria J. Sulewska
Keywords
Related Articles
- EP ID EP74030
- DOI -
- Views 175
- Downloads 0
Application of the Strongin-Sergeyev global optimization method in the compliance minimization of latticed shells. (Received in the final form January 18, 2010)
The aim of the present paper is to revisit some known truss optimization problems by applying the genuine Strongin and Sergeyev's algorithm of the global search [8]. By employing the space-filling Hilbert-Peanotype curve...
Mathematical and numerical multi-scale modelling of multiphysics problems
In this paper we discuss two multi-scale procedures, both of mathematical nature as opposed to purely numerical ones. Examples are shown for the two cases. Attention is also devoted to thermodynamical aspects such as the...
Stress analysis of linear elastic structures by the fast multipole boundary element method. (Received in the final form January 15, 2010)
In this paper a fast multipole boundary element method (FMBEM) analysis of internal stress in twodimensional linear elastic structures is presented. The expansions of the potentials occurring in the stress integral equat...
Fuzzy weight neural network in the analysis of concrete specimens and R/C column buckling tests
The paper describes the applications of back propagation neural networks with the ability to process input and output variables expressed as fuzzy numbers. The presentation of an algorithm for finding fuzzy neural networ...
An improved Neural Kalman Filtering Algorithm in the analysis of cyclic behaviour of concrete specimens
The article is related to the results of research on Node Decoupled Extended Kalman Filtering (NDEKF) as a learning method for the training of Multilayer Perceptron (MPL). Developments of this method made by the author a...