PREDICTION OF CALIFORNIA BEARING RATIO OF FINE-GRAINED SOIL STABILIZED WITH ADMIXTURES USING SOFT COMPUTING SYSTEMS
DOI:
https://doi.org/10.33736/jcest.2035.2020Keywords:
Soil, Admixtures, CBR, Regression Analysis, ANN, SVMAbstract
The main focus of this study was to predict California bearing ratio (CBR) of stabilized soils with quarry dust (QD) and lime as well as rice husk ash (RHA) and lime. In the laboratory, stabilized soils were prepared at varying mixing proportions of QD as 0, 10, 20, 30, 40 and 50%; lime of 2, 4 and 6% with varying curing periods of 0, 7 and 28 days. Moreover, admixtures of RHA with 0, 4, 8, 12 and 16%; lime of 0, 3, 4 and 5% was used to stabilize soil with RHA and lime. In this study, soft computing systems like SLR, MLR, ANN and SVM were implemented for the prediction of CBR of stabilized soils. The result of ANN reveals QD, lime and OMC were the best independent variables for the stabilization of soil with QD, while, RHA, lime, CP, OMC and MDD for the stabilization of soil with RHA. In addition, SVM proved QD and lime as well as RHA, lime, CP, OMC and MDD were the best independent variables for the stabilization of soil with QD and RHA, respectively. The optimum content of QD was found 40% and lime 4% at varying curing periods to get better CBR of stabilized soil with QD and lime. Moreover, the optimum content of RHA was also found 12% and lime 4% at varying curing periods to get better CBR of stabilized soil with RHA and lime. The observed CBR and selected independent variables can be expressed by a series of developed equations with reasonable degree of accuracy and judgment from SLR and MLR analysis. The model ANN showed comparatively better values of CBR with satisfactory limits of prediction parameters (RMSE, OR, R2 and MAE) as compared to SLR, MLR and SVM. Therefore, model ANN can be considered as best fitted for the prediction of CBR of stabilized soils. Finally, it might be concluded that the selected optimum content of admixtures and newly developed techniques of soft computing systems will further be used of other researchers to stabilize soil easily and then predict CBR of stabilized soils.
References
Venkatasubramanian, C. and Dhinakaran, G. 2011. ANN Model for Predicting CBR from Index Properties of Soils, International Journal of Civil and Structural Engineering, Integrated Publishing Association (IPA), 2 (2): 605-611.
Joel, M. and Agbedi, I. 2011. Mechanical-Cement stabilization of laterite for use as flexible pavement material, Journal of Material in Civil Engineering, Vol.23, No.2, pp.146-152.
https://doi.org/10.1061/(ASCE)MT.1943-5533.0000148
Hebib, S. and Farrell, E.R. 1999. Some Experiences of Stabilizing Irish Organic Soils. Proceedingof Dry Mix Methods for Deep Soil Stabilization (pp. 81-84).
https://doi.org/10.1201/9781315141466-11
Rajasekaran, G., and Narasimha Rao, S. 2000. Strength characteristics of lime-treated marine clay. Proceedings of the Institution of Civil Engineers-Ground Improvement, 4(3), 127-136.
https://doi.org/10.1680/grim.2000.4.3.127
Dash, S. K. and Hussain, M. 2011. Lime stabilization of soils: reappraisal. Journal of materials in civil engineering, 24(6), 707-714.
https://doi.org/10.1061/(ASCE)MT.1943-5533.0000431
Taskıran, T. 2010. Prediction of California bearing ratio (CBR) of fine grainedsoilsby AI methods, Advances in Engineering Software, Vol.41:886-892.
https://doi.org/10.1016/j.advengsoft.2010.01.003
Bhatt, S., Jain, P. K., & Pradesh, M. 2014. Prediction of California Bearing Ratio of Soils Using
Artificial Neural Network. American International Journal of Research in Science, Technology, Engineering & Mathematics, 156-16.
Sudhir, B. and Pradeep, K. J. 2014a. Prediction of California Bearing Ratio of Soils Using Artificial NeuralNetwork. American International Journal of Research in Science, Technology, Engineering &Mathematics. ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629.
Sudhir, B. and Pradeep, K. J. 2014b. Prediction of California Bearing Ratio of Soils Using Artificial Neural, Transportation Research Record (1219), 56-67.
Samui, P., Das, S. K. and Sabat, A. K. 2011. Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. International Journal of Geomechanics, 12(5), 606-611.
https://doi.org/10.1061/(ASCE)GM.1943-5622.0000129
] Afrin, H. 2017. A Review on Different Types Soil Stabilization Techniques. International Journal of Transportation Engineering and Technology, 3(2), 19.
https://doi.org/10.11648/j.ijtet.20170302.12
Jay, p., Kusum. K. and Vijay, K. 2017. Stablization of Soil Using Rice Husk Ash. International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 7, July 2017.
Rakaraddi, P. G., and Gomarsi, V. 2015. Establishing relationship between CBR with different soil properties. International journal of research in engineering and technology, 4(2), 182-188.
https://doi.org/10.15623/ijret.2015.0402023
Smarten, (2018). The SVM Classification Analysis and How Can It Benefit Business Analytics. (Source, https://www.elegantjbi.com/blog/what-is-svm-classification-analysis-and-how-can-it-benefit-business-analytics.htm, October 16, 2018).
Rajkumar, C. and Meenambal, T. (2017). Artificial neural network modelling and economic analysis of soil subgrade stavilized with flyash and geotextile. Indian journal of GEO Marine Science. Vol. 46 (07), July 2017, 1454-1461.
Sarapu, D. 2016. Potentials of Rice Husk Ash for Soil Stabilization, International Journal of Geomechanics, 12(5), 606-611.
Al-Joulani, N. 2012. Effect of stone powder and lime on strength, compaction and CBR properties of fine soils. Jordan Journal of Civil Engineering, 6(1), 1-16.
Sabat, A. K. 2013. Prediction of California bearing ratio of a soil stabilized with lime and quarry dust using artificial neural network. Electronic Journal of Geotechnical Engineering, 18, 3261-3272.
Sabat, A. K. 2015. Prediction of California bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine. Electron. J. Geotech. Eng, 20, 981-991.
Ali, B., Rahman, M. A., and Rafizul, I. M. 2016. Prediction of California Bearing Ratio of Stabilized Soil using Artificial Neural Network. Proceedings of the 3rd International Conference on Civil Engineering for Sustainable Development (ICCESD 2016), (ISBN: 978-984-34-0265-3).
Downloads
Published
How to Cite
Issue
Section
License
Upon acceptance of an article, the corresponding author on behalf of all authors will be asked to complete and upload the Copyright Transfer Form (refer to Copyright Issues for more information on this) alongside the electronic proof file.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer the publication in the Journal.
2) For open-access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds the copyright or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subjected to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) are responsible to ensure their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
5) In signing this statement, the author(s) declare that the researches which they have conducted comply with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.
