Abstract:
The compression index is one the compressibility characteristics concept to make estimates
of soil responses, when one cannot conduct sufficient soil tests completely characterize a soil
at a site. In this study, correlations are developed to predict compression index (Cc) from
index tests so that one can be able to model Jimma soils with compression index using simple
laboratory tests. The objective of the study is to predict the compression index from soil index
properties in Jimma town. Undisturbed and disturbed soil samples from fifteen different
locations of Jimma, where different clay soil is found, are collected.
Laboratory tests like specific gravity, grain size analysis, Atterberg limit and one dimensional consolidation test for thirty test samples (at 1.5 m to 3.0 m depths per each of
fifteen test pits) are conducted. From this test, compressibility soil parameters compression
index (Cc) and swell index (Cs) are determined. From the results of limited tests, an
indicative good correlation is observed between compression index and liquid limit, plastic
limit, and plasticity index. However, a poor correlation has developed between compression
index (Cc) and plastic limit (PL) when related to the other parameters. The developed
correlations will be important inputs in modeling Jimma clay soils with regression analysis
and artificial neural network model using simple index tests. The proposed model that
obtained from the correlation between Cc and LL, PI is given as Cc = 0.0018(LL) +
0.0004(PI) +0.1231, R2 = 0.847 with 0.012 of standard error through multilinear regression
analysis. In addition, the results of this study can serve as a basis for further study of such
correlations on different clay soils in the country.
The compression index of soils was mean 0.274, at least 0.227 and at most 0.33 and
depended on clay soil class. The results showed that the correlation coefficient (R
2 = 0.912
and R
2= 0.841) was determined by neural network and regression method respectively. By
comparing the values of R-value and error square mean (MSE) by two using methods, it was
revealed that artificial neural network has the least error and the most accuracy. As a result,
for estimating the compression coefficient in the study area, this method should be used
