Data compression by Principal Component Analysis (PCA) in modelling of soil density parameters based on soil granulation

Maria Jolanta Sulewska, Katarzyna Zabielska-Adamska

Abstract


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.


Keywords


Artificial Neural Networks, Principal Component Analysis, compaction parameters, minimum and maximum dry density of solid particles, graining parameters.

Full Text:

PDF