Current Research Bulletin

The California Bearing Ratio (CBR) test is one of the most widely adopted pavement subgrade evaluation methods globally, yet it is time-consuming and costly when applied to large numbers of samples. This study developed multiple linear regression analysis (MLRA) models to predict soaked and unsoaked CBR values from seven basic geotechnical index properties — maximum dry density (MDD), optimum moisture content (OMC), grain size fractions passing sieves No. 7, No. 36, and No. 200 (N7, N36, N200), liquid limit (LL), and plasticity index (PI) — for lateritic soils along the Zaria–Kano Road corridor in Kaduna and Kano States, Nigeria. Secondary data comprising 80 datasets were obtained, of which 65 were used for model training and 15 for independent validation. MATLAB R2019b was the primary modelling environment, and Minitab 17 was used for corroborative analysis. Five soaked CBR models (M1–M5) and five unsoaked CBR models (M6–M10) were developed by progressively eliminating statistically insignificant predictors. Pearson correlation analysis identified MDD and N200 as the dominant predictors for soaked CBR, while MDD and N200 were equally dominant for unsoaked CBR. The best-fit soaked CBR model (M1) incorporating all seven predictors achieved R² = 0.766 and R = 0.875, while the best unsoaked model (M6) yielded R² = 0.715 and R = 0.846. Cross-validation against 15 independent samples confirmed model reliability for both conditions. Comparison of MATLAB and Minitab outputs demonstrated consistency across software platforms. The study confirms that CBR values of lateritic soils along this corridor can be reliably predicted from index properties, significantly reducing laboratory time and costs associated with the 96-hour soaking protocol.

California Bearing Ratio, Multiple Linear Regression, Geotechnical Index Properties, Lateritic Soil, Pavement Subgrade, MATLAB, Nigeria.

1. Adebayo, K., Sani, J. E., Hamzat, M. O., Aliyu, A., Kabir, N. K., Balarabe, F., & Adeshokan, M. O. (2022). Variability of soil properties with horizontal locations. Journal of Science Technology and Education, 10(2), 47–55.
2. Akanbi, T. Y., Waziri, J. A., & Brown, D. I. (2022). Properties assessment and application of regression analysis on the compressive strength of hollow sandcrete blocks. FUOYE Journal of Engineering and Technology, 7(4), 499–504. https://doi.org/10.46792/fuoyejet.v7i4.908
3. Amadi, A. N. (2024). An assessment of the geotechnical properties of the subsoil of parts of Federal University of Technology, Minna, Gidan Kwano Campus, for foundation design and construction. Journal of Science, Education and Technology, 1(2), 87–102.
4. Arulkumaran, S., & Sukumar, S. (2015). Regression model to predict subsurface properties. International Journal of Earth Sciences Engineering, 8(2), 233–239.
5. ASTM D1883-99. (1999). Standard test method for CBR (California Bearing Ratio) of laboratory-compacted soils. American Society for Testing and Materials, West Conshohocken, PA.
6. Bassey, O. B., Attah, I. C., Ambrose, E. E., & Etim, R. F. (2017). Correlation between CBR values and index properties of soils: A case study of Ibiono, Oron and Onna in Akwa Ibom State. Resources and Environment, 7(4), 94–102. https://doi.org/10.5923/j.re.20170704.02
7. Dahiru, M., Maina, M. M., Audu, I., Wada, I. Y. T., Nasiru, N. K., & Umar, S. E. (2021). Response surface regression model for predicting clay composition and its relationship with selected soil properties at Kano River Irrigation Project, Nigeria. Duste Journal of Pure and Applied Sciences, 7(1), 14–26.
8. Datta, T. F., & Pisca, G. H. (2020). Correlation between CBR and index properties of soil. Proceedings of Indian Geotechnical Conference.
9. Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 35(1), 105–124.
10. Egbe, J. G., Ewa, D. E., Ubi, S. E., Ikwa, G. B., & Tumenayo, O. O. (2018). Application of multilinear regression analysis in modelling of soil properties for geotechnical civil engineering works in Calabar South. Nigerian Journal of Technology, 36(4), 1059–1065. https://doi.org/10.4314/njt.v36i4.10
11. Erzin, B. D. (2022). Development of correlation between different engineering properties of subgrade soil. Journal of Emerging Technologies and Innovative Research (JETIR).
12. Houcque, D. (2005). Introduction to MATLAB for engineering students. Northwestern University, August.
13. Ibrahim, F. K. (2017). Application of soft computing techniques and multiple regression models for CBR prediction of soils. International Journal, 5, 1552–1561.
14. Ishola, K., Adeyemo, K. A., & Ajayi, O. G. (2020). Assessment of UCS and CBR characteristics of some selected lateritic soils in Osun State, Nigeria for pavement construction. UI Journal of Civil Engineering and Technology, 2(2), 16–24.
15. Ishola, K., Adeyemo, K. A., Kareem, M. A., Oriaje, A. T., Olawuyi, M. Y., & Ayanlere, S. A. (2024). Regression analysis on California Bearing Ratio of selected soft soils in Osun State for pavement construction. Journal of Civil and Environmental Studies, 12.
16. Khan, M. R. (2013). Problem solving and data analysis using Minitab: A clear and easy guide to Six Sigma methodology (1st ed.). Wiley, New York.
17. Messad, S. I., & Caleb, O. M. (2020). California Bearing Ratio (CBR) test on poor lateritic soils with bone ash. International Journal of Engineering Research and Technology, 2(9), 3–10.
18. Meyerhof, G. G. (1963). Some recent research on the bearing capacity of foundations. Canadian Geotechnical Journal, 1, 16–31. https://doi.org/10.1139/t63-003
19. Naveen, B., & Shirur, V. F. (2024). Establishing relationship between CBR value and physical properties of soil. IOSR Journal of Mechanical and Civil Engineering.
20. Nwankwoala, H. O., Sani, A. K., & Belta, S. M. (2020). Geotechnical properties for foundation design and construction, Enugu State, Nigeria. American Journal of Civil Engineering and Architecture, 3(4), 142–153.
21. Okeke, O. E., Amadi, A. N., & Onoduku, U. S. (2021). Geotechnical properties of the subsoil for designing shallow foundation in some selected parts of Chanchaga area, Minna, Nigeria. Journal of Environmental Science, 1(1), 45–54.
22. Rakaraddi, P. G., & Gomarsi, V. (2015). Establishing relationship between CBR with different soil properties. International Journal of Research in Engineering and Technology, 4(2), 182–188.
23. Shirur, N. B., & Hiremath, S. G. (2014). Establishing relationship between CBR value and physical properties of soil. IOSR Journal of Mechanical and Civil Engineering, 11(5), 26–30.
24. Taylor, K. E. (1990). Correlation coefficient in regression analysis. American Journal of Statistics, 15(2), 104–112.
25. Torgano, S., Geremew, A., & Worku, A. (2020). Prediction of CBR values from index and engineering properties of sub-grade soils from Addis Ababa. Ethiopian Journal of Science and Technology, 13(1), 1–26.
26. Tseganeh, M., & Quezon, E. T. (2022). Correlation between soaked CBR and index properties of sub-grade soils. Civil Engineering and Urban Planning: An International Journal, 9(1).