Modeling CBR Value using RF and M5P Techniques

Abstract

Two modeling techniques namely (i) Random forest (RF) and (ii) M5P model tree are used to model, soaked California bearing ratio (CBR) value of thermal power plant generated stabilized pond ash. Pond ash was stabilized with the help of commercially available lime and industrial waste lime sludge. CBR data generated from exhaustive experimental programme was used in the study. Variations in doses of stabilizer i.e. lime (L) and lime sludge (LS), curing duration (CP) and proctor test results density (MDD) & moisture (OMC) are considered as input variables. Experimentally observed CBR value was used as output variable. Performance of models was measured using standard statistical parameters. Although, both the model’s performance in predicting CBR value is satisfactory however from the statistical parameters it is evident that RF method perform better in comparison to M5P model. Sensitivity analyses identify CP as the most influencing factor that affects CBR value of the stabilized pond ash.