Modeling CBR Value using RF and M5P Techniques

  • Manju Suthar Department of Civil Engineering, Maharaja Agrasen University, Baddi, India
  • Praveen Aggarwal Civil Engineering Department, National Institute of Technology, Kurukshetra, India
Keywords: random forest, M5P, CBR, pond ash, stabilization


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.


Bera, A. K., Ghosh A., and Ghosh A. 2007. Compaction characteristics of pond ash. Journal of Materials in Civil Engineering 19, 4, pp. 349–357.

Suthar M. and Aggarwal P. 2017. Analysis of heavy metals in pond ash samples from Haryana. In Proceedings of 29th Research World International Conference. Las Vegas, USA, 16th-17th March 2017.

ASTM C618-08a. 2008. Specification for fly ash and raw or calcined natural pozzolana for use as material admixture in portland cement concrete.

Parsa, J., Munson-McGee, S. H., and Steiner, R. 1996. Stabilization/solidification of hazardous wastes using fly ash. Journal of Environmental Engineering 122, 10, pp. 935–940.

Ghosh, A. and Subbarao, C. 2006. Tensile strength bearing ratio and slake durability of class F fly ash stabilized with lime and gypsum. Journal of Materials in Civil Engineering 18, 1, pp. 18–27.

Ghosh, A. 1997. Environmental and engineering characteristics of stabilized low lime fly ash. Ph.D. dissertation, Indian Institute of Technology, Kharagpur, India.

Ghosh, A. and Subbarao, C. 2007. Strength characteristics of class F fly ash modified with lime and gypsum. Journal of Geotechnical and Geoenvironmental Engineering 133, 7, pp. 757–766.

Pandian, N. S. 2004. Fly ash characterization with reference to geotechnical applications. Journal of the Indian Institute of Science 84, pp. 189–216.

Suthar, R. and Aggarwal, P. 2015. Class-F pond ash a potential highway construction material – a review. Indian Highways 43, 8, pp. 23–32.

Sahu, V. and Gayathri, V. 2014. The use of fly ash and lime sludge as partial replacement of cement in mortar. International Journal of Engineering and Technology Innovation 4, 1, pp. 30–37.

Battaglia, A., Calace, N., Nardi, E., Petronio, B. M., and Pietroletti, M. 2007. Reduction of Pb and Zn bioavailable forms in metal polluted soils due to paper mill sludge addition: Effects on Pb and Zn transferability to barley. Bioresource Technology 98, 16, pp. 2993–2999.

Calace, N, Campisi, T., Iacondini, A., Leoni, M., Petronio, B. M., and Pietroletti, M. 2005. Metal-contaminated soil remediation by means of paper mill sludges addition: chemical and ecotoxicological evaluation. Environmental Pollution 136, 3, pp. 485–492.

Mahmood, T. and Elliot, A. 2006. A review of secondary sludge reduction technology for the pulp and paper industry. Water Research 40, 11, pp. 2093–2112.

Day, W. R. 2001. Soil testing manual procedures, classification data, and sampling practices. McGraw-Hill Professional, USA.

Suthar, M. and Aggarwal, P. 2018. Predicting CBR Value of Stabilized Pond Ash with Lime and Lime Sludge Using ANN and MR Models. International Journal of Geosynthetics and Ground Engineering 4, 6, pp. 2–7. doi:

Taskiran, T. 2010. Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Advances in Engineering Software 41, 6, pp. 886–892.

Sabat, A. K. 2015. Prediction of California bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine. Electronic Journal of Geotechnical Engineering 20, pp. 981–991.

Erzin, Y. and Y, Turkoz, D. 2016. Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Computing and Applications 27, 5, pp. 1415–1426.

Pal, M., Singh, N. K., and Tiwari, N. K. 2013. Pier scour modelling using random forest regression. ISH Journal of Hydraulic Engineering 19(2), pp. 69–75.

Breiman, L. 1999. Random forests - Random Features. Technical Report 567, Statistics Department, University of California, Berkeley.

Breiman, L. 1996. Bagging predictors. Machine Learning 24, 2, pp. 123–140.

Quinlan, J. R. 1992. Learning with continuous classes. In Proceedings of Australian Joint Conference on Artificial Intelligence. World Scientific Press: Singapore, pp. 343–348.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1984. Classification and Regression Trees. Chapman and Hall/CRC, Wadsworth, Monterey, CA.

Pal, M, and Mather, P. M. 2003. An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification. Remote Sensing of Environment 86, 4, pp. 554–565.

Feller, W. 1968. An Introduction to Probability Theory and Its Application. Vol. 1, 3rd ed., Wiley, New York, USA.

Bureau of Indian Standards. 2002. IS 2720-7: Methods of test for soils, Part 7: Determination of water content-dry density relation using light compaction.

Bureau of Indian Standards. 2002. IS 2720-16: IS 2720-16: Methods of test for soils, Part 16: Laboratory determination of CBR.

How to Cite
Suthar, M. and Aggarwal, P. 2019. Modeling CBR Value using RF and M5P Techniques. MENDEL. 25, 1 (Jun. 2019), 73-78. DOI: