Regression and Neural Network Models for California Bearing Ratio Prediction of Typical Granular Materials in Egypt

  • S. Taha
  • A. GabrEmail author
  • S. El-Badawy
Research Article - Civil Engineering


California bearing ratio (CBR) is an important property used to express the quality and strength of the unbound granular materials and subgrade soils. It is one of the material inputs for the American Association State Highway Transportation Officials 1993 guide, and the Mechanistic Empirical Pavement Design Guide for the structural design of flexible pavements in case of the resilient modulus is not known. CBR is also conducted on the unbound materials for the quality control/quality assurance during construction. Because of its importance, this paper presents an attempt to develop simple and reliable CBR models based on routine material properties such as gradation, Atterberg limits and compaction properties using regression analysis (RA) and artificial neural networks (ANNs). Database of 207 CBR values was collected from the quality control reports prepared at the Highway and Airport Engineering Laboratory, Mansoura University. The collected CBR values were found to range between 26 and 98%. About 80% of the collected data were used for model development, while the remaining 20% were used for model validation in addition to 11 laboratory tested specimens. The developed model by RA and ANNs correlates CBR values with maximum dry density and diameter at 60% passing (D60). The prediction accuracy in terms of coefficient of determination (\({R}^{2}\)) for the developed CBR model by both techniques was excellent, and the validation of the suggested model was satisfactory.


California bearing ratio Correlation Compaction characteristics Gradation Regression ANNs 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yoder, E.J.; Witczak, M.W.: Principles of Pavement Design. Wiley, Hoboken (1975)CrossRefGoogle Scholar
  2. 2.
    Kin, M.W.: California bearing ratio correlation with soil index properties. Master of Engineering project, University Technology, Malaysia (2006)Google Scholar
  3. 3.
    Ramasubbarao, G.V.; Sankar, G.S.: Predicting soaked CBR value of fine grained soils using index and compaction characteristics. Jordan J. Civil Eng. 7(3), 354–360 (2013)Google Scholar
  4. 4.
    Alawi, M.H.; Rajab, M.I.: Prediction of California bearing ratio of subbase layer using multiple linear regression models. Road Mater. Pavement Des. 14(1), 211–219 (2013)CrossRefGoogle Scholar
  5. 5.
    Attique Ul-Rehman,; Farooq, K.; Mujtaba, H.; Altaf, O.: Estimation of California bearing ratio (CBR) from index properties and compaction characteristics of coarse grained soil. Sci. Int. (Lahore) 27(6), 6207–6210 (2015)Google Scholar
  6. 6.
    Rakaraddi, P.G.; Gomarsi, V.: Establishing relationship between CBR with different soil properties. Int. J. Res. Eng. Technol. 4(2), 182–188 (2015)CrossRefGoogle Scholar
  7. 7.
    Aderinola, O.S.: Predicting the California bearing ratio value of low compressible clays with it’s index and compaction characteristics. Int. J. Sci. Eng. Res. 8(5), 1460–1472 (2017)Google Scholar
  8. 8.
    Taskiran, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41(6), 886–892 (2010)CrossRefGoogle Scholar
  9. 9.
    Yildirim, B.; Gunaydin, O.: Estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. 38(5), 6381–6391 (2011)CrossRefGoogle Scholar
  10. 10.
    Harini, S.N.: Prediction CBR of fine grained soils by artificial neural network and multiple linear regression. Int. J. Civil Eng. Technol. (IJCIET) 5(2), 119–126 (2014)Google Scholar
  11. 11.
    Farias, I.G.; Araujo, W.; Ruiz, G.: Prediction of California bearing ratio from index properties of soils using parametric and non-parametric models. Geotech. Geol. Eng. 36(6), 3485–3498 (2018)CrossRefGoogle Scholar
  12. 12.
    Attique Ul-Rehman,; Farooq, K.; Mujtaba, H.: Prediction of California bearing ratio (CBR) and compaction characteristics of granular soils. Acta Geotech. Slov. 14(1), 62–72 (2017)Google Scholar
  13. 13.
    Saklecha, P.P.; Katpatal, Y.B.; Rathore, S.S.; Agarawal, D.K.: Correlation of mechanical properties of weathered Basaltic Terrain for strength characterization of foundation using ANN. Int. J. Comput. Appl. 33(10), 7–12 (2011)Google Scholar
  14. 14.
    Shirur, N.B.; Hiremath, S.G.: Establishing relationship between CBR value and physical properties of soil. IOSR J. Mech. Civil Eng. (IOSR-JMCE) 11(5), 26–30 (2014)CrossRefGoogle Scholar
  15. 15.
    Roy, S.: Assessment of soaked California bearing ratio value using geotechnical properties of soils. Resour. Environ. 6(4), 80–87 (2016)Google Scholar
  16. 16.
    Yashas, S.R.; Harish, S.N.; Muralidhara, : Effect of California bearing ratio on the properties of soil. Am. J. Eng. Res. (AJER) 5(4), 28–37 (2016)Google Scholar
  17. 17.
    Agarwal, K.B.; Ghanekar, K.D.: Prediction of CBR from plasticity characteristics of soil. In: Proceeding of 2nd South-East Asian Conference on Soil Engineering, Singapore, pp. 11–15 (1970)Google Scholar
  18. 18.
    NCHRP: Guide for mechanistic-empirical design of new and rehabilitated pavement structures. NCHRP 1-37A Final Report, Appendix CC-1: Correlation of CBR values with soil index properties (2004)Google Scholar
  19. 19.
    Abd El-Rahman, Q.B.: Correlation between California bearing ratio (CBR) results and physical properties of soils. Bachelor of civil engineering project, Technology University, Malaysia (2009)Google Scholar
  20. 20.
    Patel, R.S.; Desai, M.D.: CBR predicted by index properties for alluvial soils of South Gujarat. In: Proceedings of the Indian Geotechnical Conference, Mumbai, pp. 79–82 (2010)Google Scholar
  21. 21.
    Singh, D.; Reddy, K.S.; Yadu, L.: Moisture and compaction based statistical model for estimating CBR of fine grained subgrade soils. Int. J. Earth Sci. Eng. 4(6), 100–103 (2011)Google Scholar
  22. 22.
    Talukdar, D.K.: A study of correlation between California bearing ratio (CBR) value with other properties of soil. Int. J. Emerg. Technol. Adv. Eng. 4(1), 559–562 (2014)Google Scholar
  23. 23.
    Yadav, et al.: Prediction of soaked CBR of fine grained soils from classification and compaction parameters. Int. J. Adv. Eng. Res. Stud. III/IV, 119–121 (2014)Google Scholar
  24. 24.
    Bhatt, S.; Jain, P.K.; Pradesh, M.: Prediction of California bearing ratio of soils using artificial neural network. Am. Int. J. Res. Sci. Technol. Eng. Math. 8(2), 156–161 (2014)Google Scholar
  25. 25.
    Leliso, Y.: Correlation of CBR value with soil index properties for Addis Ababa subgrade soils. Master degree project, Addis Ababa University (2013)Google Scholar
  26. 26.
    Bassey, O.B.; Attah, I.C.; Ambrose, E.E.; Etim, R.K.: Correlation between CBR values and index properties of soils? A case study of Ibiono, Oron and Onna in Akwa Ibom State. Resour. Environ. 7(4), 94–102 (2017)Google Scholar
  27. 27.
    Aderinola, O.S.; Oguntoyinbo, E.; Quadri, A.I.: Correlation of California bearing ratio value of clays with soil index and compaction characteristics. Int. J. Sci. Res. Innov. Technol. 4(4), 12–22 (2017)Google Scholar
  28. 28.
    Abdella, D.; Abebe, T.; Quezon, E.T.: Regression analysis of index properties of soil as strength determinant for California bearing ratio (CBR). Glob. Sci. J. 5(6), 1–12 (2017)Google Scholar
  29. 29.
    Fairbrother, S.: Estimating forest road aggregate strength by measuring fundamental aggregate properties. In: Proceeding of 34th Council on Forest Engineering, pp. 1–9 (2011)Google Scholar
  30. 30.
    Taha, S.; El-Badawy, S.; Ali, A.: Determination of California bearing ratio through soil index properties. In: 4th Jordan International Conference and Exhibition for Roads and Transport (JITC4) (2014)Google Scholar
  31. 31.
    Taha, S.; El-Badawy, S.; Gabr, A.; Azam, A.; Shahida, U.: Modeling of California bearing ratio using basic engineering properties. In: Proceeding of 8th International Conference of Engineering, Mansoura-sharm El sheikh, pp. 1–10 (2015)Google Scholar
  32. 32.
    Araujo, W.; Ruiz, G.: Correlation equations of CBR with index properties of soil in the city of Piura. In: Proceeding of 14th LACCEI International Multi-conference for Engineering, Education and Technology, pp. 1–7 (2016)Google Scholar
  33. 33.
    Vadi, P.K.; Manjula, Ch; Poornima, P.: Artificial neural networks (ANNS) for prediction of California bearing ratio of soils. Int. J. Mod. Eng. Res. 5(1), 15–21 (2015)Google Scholar
  34. 34.
    Ali, A.; El Rahman, B.; Rafizul, I.M.: Prediction Of California bearing ratio of stabilized soil using artificial neural network. In: Proceeding of 3rd International Conference on Civil Engineering for Sustainable Development, pp. 978–984 (2016)Google Scholar
  35. 35.
    ECP (Egyptian Code of Practice): Egyptian code of practice for urban and rural roads, ed 1: road materials and their tests (Part Four). Ministry of Housing, Utilities and Urban Communities, Cairo, Egypt (2008)Google Scholar
  36. 36.
    AASHTO T27-14: Standard Method of Test for Sieve Analysis of Fine and Coarse Aggregates Sieve Analysis of Fine and Coarse Aggregates, vol. 14 (2018)Google Scholar
  37. 37.
    AASHTO T 89-13: Standard Method of Test for Determining the Liquid Limit of Soils, vol. 13 (2017)Google Scholar
  38. 38.
    AASHTO T 180-18: Standard Method of Test for Moisture—Density Relations of Soils-Using a 4.54-kg (10-lb) Rammer and a 457-mm (18-in .). Drop moisture—density relations of soils (2018)Google Scholar
  39. 39.
    AASHTO T 193-13: Standard Method of Test for the California Bearing Ratio, vol. 13 (2017)Google Scholar
  40. 40.
    Akash, M.S.: Artificial intelligence & neural networks. In: Proceeding of 31th IRF International Conference, India, pp. 53–60 (2015)Google Scholar
  41. 41.
    Principe, J.S.; Euliano, N.R.; Lefebvre, W.C.: Neural and Adaptive Systems: Fundamentals Through Simulations, vol. 672. Wiley, Hoboken (2000)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Public Works Engineering Department, Faculty of EngineeringMansoura UniversityMansouraEgypt

Personalised recommendations