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

  • Research Article - Civil Engineering
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Abstract

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.

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References

  1. Yoder, E.J.; Witczak, M.W.: Principles of Pavement Design. Wiley, Hoboken (1975)

    Book  Google Scholar 

  2. Kin, M.W.: California bearing ratio correlation with soil index properties. Master of Engineering project, University Technology, Malaysia (2006)

  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. 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)

    Article  Google Scholar 

  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. Rakaraddi, P.G.; Gomarsi, V.: Establishing relationship between CBR with different soil properties. Int. J. Res. Eng. Technol. 4(2), 182–188 (2015)

    Article  Google Scholar 

  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. Taskiran, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41(6), 886–892 (2010)

    Article  Google Scholar 

  9. Yildirim, B.; Gunaydin, O.: Estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. 38(5), 6381–6391 (2011)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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)

  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)

  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)

  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)

  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. 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. 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)

  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. Leliso, Y.: Correlation of CBR value with soil index properties for Addis Ababa subgrade soils. Master degree project, Addis Ababa University (2013)

  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. 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. 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. Fairbrother, S.: Estimating forest road aggregate strength by measuring fundamental aggregate properties. In: Proceeding of 34th Council on Forest Engineering, pp. 1–9 (2011)

  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)

  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)

  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)

  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. 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)

  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)

  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)

  37. AASHTO T 89-13: Standard Method of Test for Determining the Liquid Limit of Soils, vol. 13 (2017)

  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)

  39. AASHTO T 193-13: Standard Method of Test for the California Bearing Ratio, vol. 13 (2017)

  40. Akash, M.S.: Artificial intelligence & neural networks. In: Proceeding of 31th IRF International Conference, India, pp. 53–60 (2015)

  41. Principe, J.S.; Euliano, N.R.; Lefebvre, W.C.: Neural and Adaptive Systems: Fundamentals Through Simulations, vol. 672. Wiley, Hoboken (2000)

    Google Scholar 

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Authors and Affiliations

  1. Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    S. Taha, A. Gabr & S. El-Badawy

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  1. S. Taha
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  2. A. Gabr
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  3. S. El-Badawy
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Correspondence to A. Gabr.

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Taha, S., Gabr, A. & El-Badawy, S. Regression and Neural Network Models for California Bearing Ratio Prediction of Typical Granular Materials in Egypt. Arab J Sci Eng 44, 8691–8705 (2019). https://doi.org/10.1007/s13369-019-03803-z

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  • DOI: https://doi.org/10.1007/s13369-019-03803-z

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