Modification in Strength Parameter (CBR) of Sub-Grade Soil with Addition of Fly Ash
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The determination of the California Bearing Ratio value of soil is tiresome, uneconomical, and time-consuming in the laboratory. Therefore, there is a required automation system to determine the California Bearing Ratio value of soil. Machine learning algorithms are being used for automation systems. In this paper, Artificial Neural Network has been proposed for the prediction of the California Bearing Ratio value of soil. Ash percentage, Liquid Limit, Plastic_Limit, Plasticity index, Shrinkage Limit, MDD and OMC parameters of soil affect the value of the California Bearing Ratio. In the laboratory, the training dataset has generated using these parameters of soil. The proposed classifier has been trained and tested using the training and testing dataset. Experimental results show that the proposed Artificial Neural Network is very accurate to predict California Bearing Ratio values of soil. It is also observed that the linear regression algorithm is very easy and useful to determine the value of the California Bearing ratio depending on seven attributes of soil. The rules generated by J48 and PART can be used to determine the California Bearing ratio. These models are very useful for civil engineers and civil constructors as a California Bearing ratio prediction automation system.
Asefachew Belete Tseganeh and Emer Tucay Quezon, "Prediction of Sub grade Strength from Index Properties of Expansive Soil Stabilized with Bagasse Ash and Calcined Termite Clay Powder Using Artificial Neural Network and Regression, Hindawi, Advances in Civil Engineering, Volume 2022, Article ID 9186567, 15 pages.
E. Assefa, B. Ayehutsega, and C. Sachpazis, “Experimental investigation on the microstructural properties of black cotton soil stabilized with cinder (scoria) fines and class- C fly ash,” International Journal of Engineering Research in Africa 5, vol. 56, 2020.
M. Zumrawi, “Swelling potential of compacted expansive soils,” International Journal of Engineering Research and Technology, vol. 2, pp. 1–6, 2013.
C. Rajakumar, P. K. R. Rao, G. R. Babu, and A. Sreenivasulu, “Experimental and numerical prediction of California bearing ratio of expansive soil stabilized by bagasse ash and geotextile reinforcement,” IOP Conference Series: Earth and Environmental Science, vol. 796, Article ID 012057, 2021.
T. F. Goshu, “Optimization of bagasse ash to cement mix proportion for M30 Grade concrete,” National Academic Digital Repository of Ethiopia (NADRE), 2019.
I. Taleb Bahmed, K. Harichane, M. Ghrici, B. Boukhatem, R. Rebouh, and H. Gadouri, “Prediction of geotechnical properties of clayey soils stabilized with lime using artificial neural networks (ANNs),” International Journal of Geotechnical, Engineering, vol. 13, no. 2, pp. 191–203, 2017.
Amir Hossein Alavi_, Amir Hossein Gandomi, and Ali Mollahasani, "A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil," Variants of Evolutionary Algorithms for Real-World Applications, 2012 Springer- Verlag Berlin Heidelberg ISBN 978-3-642-23423-1.
Jyoti S.Trivedi, Sandeep Nair and Chakradhar Iyyunni, " Optimum Utilization of Fly Ash for Stabilization of Sub-Grade Soil using Genetic Algorithm," Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference, Procedia Engineering 51 ( 2013 ) 250 – 258.
S. Levasseur, Y. Malécot, M. Boulon and E. Flavigny, "Soil parameter identification using a genetic algorithm," International Journal for Numerical and Analytical Methods in Aeromechanics, Vol. 32, Issue 2, Pages 189-213,10 February 2008.
Anjita N A, Christy Antony George and Sowmya.V. Krishnankutty, " Prediction of Maximum Dry Density of Soil using Genetic Algorithm," International Journal of Engineering Research & Technology Vol. 6 Issue 03, March-2017.
M H Bagheripour, I Shooshpasha and M Afzalirad, "A genetic algorithm approach for assessing soil liquefaction potential based on reliability method," J. Earth Syst. Sci. 121, No. 1, February 2012, pp. 45–62.
Lijie Cui, Daichao Sheng, " Genetic algorithms in probabilistic finite element analysis of geotechnical problems," Computers and Geotechnics, Volume 32, Issue 8, December 2005, Pages 555-563.
Aurélie Papon, Yvon Riou, Christophe Dano, Pierre Yves Hicher, "Single‐and multi‐objective genetic algorithm optimization for identifying soil parameters," International Journal for Numerical and Analytical Methods in Geo-mechanics 36(5):597 - 618, April 2012.
Jin-Lee Kim, "Integrated genetic algorithm and its applications for construction resource optimization," Proceedings of the 2010 Winter Simulation Conference, 2010, pp. 3212-3219, doi: 10.1109/WSC.2010.5679013.
Mitali S Mhatre, Fauzia Siddiqui, Mugdha Dongre and Paramjit Thakur, "A Review paper on Artificial Neural Network: A Prediction Technique," International Journal of Scientific & Engineering Research, Volume 6, Issue 12, December-2015 ISSN 2229-5518
Yan-yan Song and Ying Lu, "Decision tree methods: applications for classification and prediction,"Shanghai Arch Psychiatry. 2015 Apr 25; 27(2): 130–135.
Elbe Frank and Ian Witten, “Generating Accurate Rules Sets without Global Optimization,” Department of Computer Science Technical report, University of Waikato, Hamilton, New Zealand, January 1990.
B. Lakshmi, T. Indumathi, and N. Ravi, “A study on c. 5 decision tree classification algorithm for risk predictions during pregnancy,” Procedia Technology, vol. 24, pp. 1542–1549, 2016.
R. Quinlan and R. Kohavi, “Decision tree discovery.” Data Mining, 1999.
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