Problem
Statement/Abstract:
The
overall goal of my part in this project is to compare wet sieving to,
both manually and automated image analysis techniques, to find the
grain size distribution (GSD) within a given sample. The GSD is
described as the percentage or amount (in weight) of particles in a
given sample of soil, sediment, rock, sand, etc. For my portion of
the project I am focusing on comparing wet sieving methods to image
analysis techniques. Wet sieving is a procedure that is used to
evaluate grain size distribution of a given sample. It is also used
to prepare substances for analysis by removing some fine grains that
could impact the separation process.
When
using image analysis techniques we can compare it manually and
automated. For the manual side of this project we use a software
program called ImageJ, for the automated side we are using the
wavelet method (which is a mathematical function used in digital
signal processing but most relatively used for image compression) and
python coding. When analyzing the different methods we will be
comparing multiple different soil types and compositions to try and
find the best results. The ultimate goal of this project that we are
working on is to find the trafficability of a surface and the
stability and compaction of a surface, using the measurements of
grain size distribution and density found in experiments that we will
be conducting. After experiments are conducted from dry sieving they
will then be compared to see which method of sieving is more accurate
and efficient.
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