Prediction of California Bearing Ratio by Multi-Expression Programming

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V16N2-1Narongdej (1-13).pdf (ภาษาไทย)
Published: Dec 31, 2023
Keywords:
California Bearing Ratio multi-expression programming artificial neural network artificial intelligence

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Narongdej Intaratchaiyakit
Rajamangala University of Technology Tawan-ok, Uthenthawai Campus 225 Phayathai Road Pathumwan, Bangkok 10330, Thailand

Abstract

California Bearing Ratio (CBR) is a crucial experiment for civil work, such as dams and pavement. CBR is always experimental in the lab, using time-consuming, expensive, and challenging processes that have an effect on how quickly building projects move forward. Hence, the model development used six multi-expression programming (MEP) models that had diverse input variables. Model I was the most proper MEP model because Model 1's coefficients of determination were relatively high in the training and testing sets. When the MEP model is compared to the multiple linear regression (MLR) and artificial neural network (ANN) models, the MEP model is found to be the most suitable model, and the MEP model can be employed because of its high level of reliability. The mathematical equation from the MEP model is applied to predict the CBR values for preliminary design in geotechnical work to reduce cost and time.


 

Article Details

How to Cite
Intaratchaiyakit, N. (2023). Prediction of California Bearing Ratio by Multi-Expression Programming. Rajamangala University of Technology Tawan-Ok Research Journal, 16(2), 1–13. Retrieved from https://li01.tci-thaijo.org/index.php/researchjournal2rmutto/article/view/257848
Issue
Vol. 16 No. 2 (2566): Vol.16 No.2 (๋June - December 2023)
Section
Research article
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Author Biography

Narongdej Intaratchaiyakit, Rajamangala University of Technology Tawan-ok, Uthenthawai Campus 225 Phayathai Road Pathumwan, Bangkok 10330, Thailand

Faculty of Engineering and Architecture Department of Civil Engineering 

References

Abdalla, A., & Mohammed, A. S. (2022). Surrogate Models to Predict the Long-Term CompressiveStrength of Cement-Based Mortar Modified with Fly Ash. Arch. Computat Methods Eng., 29:4187-4212. https://doi.org/10.1007/s11831-022-09734-7

Abdalla, A., & Salih, A. (2022). Implementation of multi-expression programming (MEP), artificialneural network (ANN), and M5P-tree to forecast the compression strength cement-based mortar modified by calcium hydroxide at different mix proportions and curing ages. innov.Infrastruct. Solut., 7(2): 153. https://doi.org/10.1007/s41062-022-00761-8

Agarwal, K.B. & Ghanekar, K.D. (1970). Prediction of CBR from plasticity characteristics of soil. In:Proceeding of 2nd south-east Asian conference on soil engineering, Singapore. June 11-15. Bangkok: Asian Institute of Technology. 571-576.

Alawi, M. & Rajab, M. (2013). Prediction of California bearing ratio of subbase layer using multiple linear regression models. Road Mater. Pavement Des., 14(1): 211-219. DOI: 10.1080/14680629.2012.757557

Amin, M.N., Khan, K., Javed, M.F., Ewais, D.Y.Z., Qadir, M.G.; Faraz, M.I., Alam, M.W., Alabdullah, A.A., &Imran, M. (2022). Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming. MATLS., 15: 3808. https://doi.org/10.3390/ma15113808

Basma, A. A. & Kallas, N. (2004). Modeling soil collapse by artificial neural networks. Geotech., 22:427-438.

Bhatt, S., Jain, P. K., & Pradesh, M. (2014). Prediction of California bearing ratio of soils using artificial neural network. AIJRSTEM., 8(2): 156-161.

Das, S. K. & Basudhar, P. (2008). Prediction of residual friction angle of clay artificial neural network.Eng Geol., 142-145.

Erzin, Y. & Turkoz, D. (2016). Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput., 27: 1415-1426.

Fallahpour, A., Wong, K. Y., Rajoo, S., & Tian, G. (2021). An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming. J. Clean. Prod., 283: 125287. https://doi.org/10.1016/j.jclepro.2020.125287

Ferede, Z.W. (2010). Prediction of California Bearing Ratio (CBR) value from Index Properties of Soil. M.Sc. dissertation, Department of Civil Engineering, Addis Ababa Institute of Technology (AAIT).

Gandomi, A. H., Alavi, A. H., & Yun, G. J. (2011). Formulation of uplift capacity of suction caissons using multi expression programming. KSCE J. Civ. Eng., 15(2): 363-373. https://doi.org/10.1007/s12205-011-1117-9

Goldberg, D.E. 2006. Genetic Algorithms, Pearson Education India: London, UK.

Gunaydin, O., Gokoglu, A. & Fener, M. (2010). Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks. Adv. Eng. Softw., 41:1115-1123.

González Farias, I., Araujo, W., & Ruiz, G. (2018). Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models. Geotech., 36: 3485-3498.

Intaratchaiyakit, N. (2023). Estimation of California Bearing Ratio by Artificial Neural Network. RMUTTORJ., 16(1): 1-10. (In Thai).

Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press: Cambridge, MA, USA, Volume 1.

Lakshmi, S.M., Subramanian, M. S., Lalithambikhai, P., Vela, A. M. & Ashni, M. (2016). Evaluation of soaked and unsoaked CBR values of soil based on the compaction characteristics. MJCE., 28(2): 172-182.

Ma, X. & Liu, Z-b. (2016). Predicting the Oil Well Production Based on Multi Expression Programming. The Open Petroleum Engineering Journal, 9(1): 21-32.

MEPX software. (2022). MEPX software. Access 5 Jan 2022, from https://mepx.org/mepx_software.html.

Najjar, Y. M., Basheer, I. A., & Naouss, W. A. (1996). On the identification of compaction characteristics by neuronets. Comput Geotech., 18(3): 167-187.

Oltean, M. & Grosan, C. (2003). A comparison of several linear genetic programming techniques.Complex Syst., 14: 285-314.

Onyelowe, K.C., Jalal, F.E., & Iqbal, M. (2022). Intelligent modeling of unconfined compressive strength (UCS) of hybrid cement-modified unsaturated soil with nanostructured quarry fines inclusion. Innov. Infrastruct. Solut., 98(7). https://doi.org/10.1007/s41062-021-00682-y

Pala, M. (2006). A new formulation for distortional buckling stress in cold-formed steel members. J Constr Steel Res., 62: 716-722.

Park, H. I., Kweon, G. C., & Lee, S. R. (2009). Prediction of resilient modulus of granular subgrade soilsand subbase materials using artificial neural network. Road Mater. Pavement Des., 10(3):647-665.

Patel, R. S. & Desai, M.D. (2010). CBR Predicted by Index Properties of Soil for Alluvial Soils of South Gujarat. IGC, Proc. IGC, 1: 79-82.

Ramasubbarao, G. V. & Sankar, G. S. (2013). Predicting Soaked CBR Value of Fine Grained Soils Using Index and Compaction Characteristics. Jordan J. Civ. Eng., 7(3): 354-360.

Rafiq M. Y., Bugmann, G., & Easterbrook, D. J. (2001). Neural network design for engineering applications. Comput Struct., 79: 1541-1552.

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

Rehman, A.U., Farooq, K., Mujtaba, H., & Altaf, O. (2015). Estimation of California bearing ratio (CBR) from index properties and compaction characteristics of coarse-grained soil. Sci. Int. Lahore, 27(6): 6207-6210.

Rehman, A.U., Farooq, K., & Mujtaba, H. (2017). Prediction of California bearing ratio (CBR) and compaction characteristics of granular soils. Acta Geotech. Slov., 14(1): 62-72.

Roy, T.K., Chattopadhyay, B.C., & Roy, S.K. (2009). Prediction of CBR from Compaction Characteristics of Cohesive Soil. highw. res. J., July-Dec 2009. 77-88.

Shahin, M.A. & Jaksa, M. (2002). Artificial neural network-based settlement prediction formula for shallow foundations on granular soils. Aust. Geomech., 45-52.

Shahin, M.A. (2015). A review of artificial intelligence applications in shallow foundations. Int. J.Geotech., 9: 49-60.

Singh, D., Reddy, K.S., & Yadu, L. (2011). Moisture and Compaction Based Statistical Model for Estimating CBR of Fine-Grained Sub grade Soils. Int. J. Earth Sci. Eng., 4(6): 100-103.

Sivrikaya, O., Kayadelen, C., & Cecen, E. (2013). Prediction of the compaction parameters for coarse-grained soils with fines content by MLR and GEP. Acta Geotech., 2: 29-41.

Taha, S., Gabr, A. & El-Badawy, S. (2019). Regression and Neural Network Models for California Bearing Ratio Prediction of Typical Granular Materials in Egypt. Arab J Sci Eng., 44: 8691-8705.

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

Wai, K. M. (2006). California bearing ratio correlation with soil index properties master of engineering (civil-geotechnics). Faculty of Civil Engineering, University Teknology Malaysia.

Wang, H.-L., & Yin, Z.-Y. (2020). High performance prediction of soil compaction parameters using multi expression programming. Eng. Geol., 276, 105758. https://doi.org/10.1016/j.enggeo.2020.105758.

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

Zhiyuan G., Yongxian W., & Lan N. (2000). Genetic algorithms based on bin tree structure encoding. J Tsinghua Univ., 40(10): 125-128.

Zumrawi, M. (2012). Prediction of CBR from index properties of cohesive soils. Conference: 2nd International Conference on Civil Engineering and Building Materials (CEBM 2012), 17-18 Nov, 2012. At: Hong KongVolume: Adv. Civ. Eng. - Edited by Shuenn - Yih Chang, Suad Khalid Al Bahar & Jingying Zhao.