@article{article_449644, title={Regression Analysis of Soil Compaction Parameters Using Support Vector Method}, journal={Celal Bayar University Journal of Science}, volume={14}, pages={443–447}, year={2018}, DOI={10.18466/cbayarfbe.449644}, author={Günaydın, Osman and Özbeyaz, Abdurrahman and Söylemez, Mehmet}, keywords={Compaction,Maximum Dry Weight,Optimum Moisture Content,SVM,Regression}, abstract={<p class="MsoNormal" style="margin-top:0cm;margin-right:14.1pt;margin-bottom:.0001pt;margin-left:21.3pt;text-align:justify;"> <span lang="en-us" xml:lang="en-us">Some challenging studies are experimentally applied
for characterizing parameters in Proctor compaction tests. Compression of a
fill is mechanically done in Compaction process. Compaction is a physical
process which gets the soil into a dense state. Improving the shear strength
and decreasing the compressibility and permeability of the soil can be done
with this physical process. Support Vector Machine (SVM) is a popular method
due to its performance today. This method is commonly employed in the
regression analysis as well as being used in the classification process. In
this study, SVM was employed to predict of compaction parameters (maximum dry
unit weight and optimum moisture content) without making any experiments in a soil
laboratory. In the study, more than a hundred compaction data collected from
the small dams in central Anatolia region was employed. In the study, R errors
are satisfied (0.92 and 0.89) for SVM models. Consequently, the proposed
regression analysis with SVM is useful for model design of the projects in where
there are limitations as financial and temporal. </span> </p> <p> </p>}, number={4}, publisher={Manisa Celal Bayar University}