Geomechanics and Engineering

  • 제33권2호
  • /
  • Pages.183-194
  • /
  • 2023
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model

  • Mintae Kim (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Seyma Ordu (Department of Environmental Engineering, Tekirdag Namik Kemal University) ;
  • Ozkan Arslan (Department of Electronics and Communication Engineering, Tekirdag Namik Kemal University) ;
  • Junyoung Ko (Department of Civil Engineering, Chungnam National University)
  • 투고 : 2022.11.28
  • 심사 : 2023.04.05
  • 발행 : 2023.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R2), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R2 of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R2 of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

키워드

과제정보

This work was supported by research fund of Chungnam National University and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1C1C1011477).

참고문헌

  1. Agarwal, K.B. and Ghanekar, K.D. (1970), "Prediction of CBR from plasticity characteristics of soil", Proceeding of the 2nd south-east Asian conference on soil engineering, Singapore, June.
  2. Alam, S.K., Mondal, A. and Shiuly, A. (2020), "Prediction of CBR value of fine grained soils Bengal Basin by genetic expression programming, artificial neural network and krigging method", J. Geological Soc. India, 95(2), 190-196. https://doi.org/10.1007/s12594-020-1409-0.
  3. Alawi, M. and Rajab, M. (2013), "Prediction of California bearing ratio of subbase layer using multiple linear regression models", Road Mater. Pavement Design, 14(1), 211-219. https://doi.org/10.1080/14680629.2012.757557.
  4. American Association of State Highway and Transportation Officials (2021), T193-13, Standard method of test for the California bearing ratio. American Association of State Highway and Transportation Officials, Washington, DC, USA.
  5. American Society for Testing and Materials. (2021), ASTM D1883-21, Standard test method for California bearing ratio (CBR) of laboratory-compacted soils. ASTM International, West Conshohocken, PA, USA. https://doi.org/10.1520/D1883-21.
  6. Ardakani, A. and Kordnaeij, A. (2019), "Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm", Eur. J. Environ. Civil Eng., 23(4), 449-462. https://doi.org/10.1080/19648189.2017.1304269.
  7. Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M. and Trnavac, D. (2020), "ANN-based techniques in predicting cohesion of sandy-soil combined with fiber", Geomech. Eng., 20(3), 191-205. https://doi.org/10.12989/gae.2020.20.3.191.
  8. Awad, M. and Khanna, R. (2015), Support vector regression. In Efficient learning machines. Apress, Berkeley, CA, USA.
  9. Bowles, J.E. (1992), Engineering properties of soils and their measurements. McGraw-Hill, Inc., New York, NY, USA.
  10. Elbaz, K., Shen, S.L., Zhou, A., Yin, Z.Y. and Lyu, H.M. (2021), "Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network", Engineering, 7(2), 238-251. https://doi.org/10.1016/j.eng.2020.02.016.
  11. Farlow, S.J. (2020), Self-organizing methods in modeling: GMDH type algorithms. CRC Press, Inc., Boca Raton, FL, USA.
  12. Hao, S. and Pabst, T. (2022), "Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models", Acta Geotechnica, 17(4), 1383-1402. https://doi.org/10.1007/s11440-022-01472-1.
  13. Harini, H. and Naagesh, S. (2014), "Predicting CBR of fine grained soils by artificial neural network and multiple linear regression", Int. J. Civil Eng., 5(2), 119-126.
  14. Ivakhnenko, A.G. (1968), "The group method of data handling-a rival of the method of stochastic approximation", Soviet Automatic Control, 13(3), 43-55.
  15. Katte, V.Y., Mfoyet, S.M., Manefouet, B., Wouatong, A.S.L. and Bezeng, L.A. (2019), "Correlation of california bearing ratio (CBR) value with soil properties of road subgrade soil", Geotech. Geol. Eng., 37, 217-234. https://doi.org/10.1007/s10706-018-0604-x.
  16. Khatri, D.P., Acharya, I.P. and Dhakal, B.B. (2019), "Correlation of California bearing ratio with index properties of sub-grade soil: a case study on Thankot Chitlang road section", Proceedings of the IOE Graduate Conference, Kathmandu, Nepal, December.
  17. Kim, M., Okuyucu, O., Ordu, E., Ordu, S., Arslan, O. and Ko, J. (2022), "Prediction of undrained shear strength by the GMDH-type neural network using SPT-value and soil physical properties", Materials, 15(18), 6385. https://doi.org/10.3390/ma15186385.
  18. Kondo, T. (1998), "GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem", Proceedings of the 37th SICE Annual Conference. International Session Papers, Chiba, Japan, July.
  19. Li, R.Y.M., Fong, S. and Chong, K.W.S. (2017), "Forecasting the REITs and stock indices: group method of data handling neural network approach", Pacific Rim Property Res. J., 23(2), 123-160. https://doi.org/10.1080/14445921.2016.1225149.
  20. Liu, J., Luan, H., Zhang, Y., Sakaguchi, O. and Jiang, Y. (2020), "Prediction of unconfined compressive strength ahead of tunnel face using measurement-while-drilling data based on hybrid genetic algorithm", Geomech. Eng., 22(1), 81-95. https://doi.org/10.12989/gae.2020.22.1.081.
  21. Luat, N., Lee, K. and Thai, D. (2020a), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), 385-397. https://doi.org/10.12989/gae.2020.20.5.385.
  22. Luat, N., Nguyen, V., Lee, S. and Lee, K. (2020b), "An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils", Geomech. Eng., 21(6), 583-598. https://doi.org/10.12989/gae.2020.21.6.583.
  23. Madala, H.R. and Ivakhnenko, A.G. (1994), Inductive learning algorithms for complex systems modeling. CRC Press, Inc., Boca Raton, FL, USA.
  24. Montgomery, D.C., Peck, E.A. and Vining, G.G. (2021), Introduction to linear regression analysis. John Wiley & Sons, Hoboken, NJ, USA.
  25. Murtagh, F. (1991), "Multilayer perceptrons for classification and regression", Neurocomputing, 2(5-6), 183-197. https://doi.org/10.1016/0925-2312(91)90023-5.
  26. National Cooperative Highway Research Program (NCHRP). (2001), Guide for mechanistic and empirical - design for new and rehabilitated pavement structures, final document. In: Appendix CC-1: correlation of CBR values with soil index properties. Champaign, Illinois, USA.
  27. Nagaraj, H.B. and Suresh, M.R. (2018), "Influence of clay mineralogy on the relationship of CBR of fine-grained soils with their index and engineering properties", Transport. Geotech., 15, 29-38. https://doi.org/10.1016/j.trgeo.2018.02.004.
  28. Oh, S.K. and Pedrycz, W. (2002), "The design of self-organizing polynomial neural networks", Inform. Sci., 141(3-4), 237-258. https://doi.org/10.1016/S0020-0255(02)00175-5.
  29. Ravichandra, A.H., Shivakumar, K., Vinaykumar, H., Khalid, S. M. and Basavaraj, B. (2019) "Prediction of CBR value by using index properties of soil", Int. Res. J. Eng. Technol., 6(7), 3740-3747.
  30. Rakaraddi, P.G. and Gomarsi, V. (2015), "Establishing relationship between CBR with different soil properties", Int. J. Res. Eng. Technol., 4(2), 182-188. https://doi.org/10.15623/ijret.2015.0402023
  31. Ramasubbarao, G. and Sankar, S.G. (2013), "Predicting soaked CBR value of fine grained soils using index compaction characteristics", Jordan J. Civil Eng., 7(3), 354-360.
  32. Satyanarayana Reddy, C.N.V and Pavani, K. (2006), "Mechanically stabilized soils-regression equation for CBR evaluation", Proceeding of the Indian geotechnical conference, Chennai, India, December.
  33. Schmidhuber, J. (2015), "Deep learning in neural networks: An overview", Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003.
  34. Smola, A. J. and Scholkopf, B. (2004), "A tutorial on support vector regression", Statist. Comput., 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
  35. Taha, S., Gabr, A. and El-Badawy, S. (2019), "Regression and neural network models for California bearing ratio prediction of typical granular materials in Egypt", Arabian J. Sci. Eng., 44(10), 8691-8705. https://doi.org/10.1007/s13369-019-03803-z.
  36. Tenpe, A.R. and Patel, A. (2020), "Application of genetic expression programming and artificial neural network for prediction of CBR", Road Mater. Pavement Design, 21(5), 1183-1200. https://doi.org/10.1080/14680629.2018.1544924.
  37. Yildirim, B. (2009), Determination of California bearing ratio (CBR) by using regression analyses and artificial neural networks. Nigde University.
  38. Yildirim, B. and Gunaydin, O. (2011), "Estimation of California bearing ratio by using soft computing systems", Exp. Syst. Appl., 38(5), 6381-6391. https://doi.org/10.1016/j.eswa.2010.12.054.
  39. Venkatasubramanian, C. and Dhinakaran, G. (2011), "ANN model for predicting CBR from index properties of soils", Int. J. Civil Struct. Eng., 2(2), 614-620.