- Tytuł:
- Data compression by Principal Component Analysis (PCA) in modelling of soil density parameters based on soil granulation
- Autorzy:
-
Sulewska, M. J.
Zabielska-Adamska, K. - Powiązania:
- https://bibliotekanauki.pl/articles/2060294.pdf
- Data publikacji:
- 2015
- Wydawca:
- Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy
- Tematy:
-
Artificial Neural Networks
principal component analysis
compaction parameters
minimum and maximum dry density of solid particles
graining parameters - Opis:
- The parameter for the density specification of naturally compacted non-cohesive soils and soils in embankments of hydraulic structures is the density index (ID). The parameter used to control the quality of compaction of cohesive and non-cohesive soils artificially thickened, embedded in a variety of embankments is the degree of compaction (IS). In order to determine the parameters of density (ID or IS), compaction parameters ( or should be examined in a laboratory, which often is a long and difficult procedure to carry out. Therefore, there is a need for methods of improving and shortening the test of compaction parameters based on the development and application of useful correlations. Since compaction parameters are dependent on the soil granulation, a method based on regression and artificial neural networks was applied to develop required correlations. Due to the large number of input variables of neural networks in relation to the number of case studies, a PCA method was used to reduce the number of input variables, which resulted in reduction in the size of neural networks.
- Źródło:
-
Geological Quarterly; 2015, 59, 2; 400--407
1641-7291 - Pojawia się w:
- Geological Quarterly
- Dostawca treści:
- Biblioteka Nauki
Artykuł