International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 11, No. 6, 8 Dec. 2021
Cover page and Table of Contents: PDF (size: 462KB)
Application of Artificial Neural Networking Technique to Predict the Geotechnical Aspects of Expansive Soil: A Review
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Author(s)
Goutham D.R.
1,*
A.J.Krishnaiah
1
Index Terms
Artificial neural networks, Expansive soils, Free swell index and Swelling pressure.
Abstract
Soil mechanics problems deal with various types of soil that exhibit erratic behaviour in the real world, one such soil being the expansive soil where it takes a lot of laboratory test procedures to ascertain the physical properties of this soil. Modeling the behaviour of the expansive soil is complex and sometimes beyond the aptitude of most traditional procedures of physically-based engineering approaches. Artificial neural networks (ANN) are the ones used for predicting the complex nature of the soil since it has shown superior predictive potential as compared to the conventional approaches. This review aims to deliver and discuss the numerous applications of artificial neural network technique accomplished by various researchers in the field of geotechnical engineering to predict several properties of the expansive soil such as free swell index, unconfined compressive strength, shear strength of the soil, swelling pressure and swell percent, compaction characteristics, and plasticity index. This paper will assist practising engineers in determining the best modelling approaches and formulating the necessary data for using the ANN technique to solve soil mechanics problems.
Cite This Paper
Goutham D.R., A.J.Krishnaiah, " Application of Artificial Neural Networking Technique to Predict the Geotechnical Aspects of Expansive Soil: A Review ", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.6, pp. 48-53, 2021. DOI: 10.5815/ijem.2021.06.05
Reference
[1]Mohamed Shahin, A. (2016). “State-of-the-art review of some artificial intelligence applications in pile foundations”, Geosci. Front., vol. 7, no. 1, pp. 33–44.
[2]Kumar Dutta, R., Singh, A., and Gnananandarao, T. (2019). “Prediction of Free Swell Index for the Expansive Soil Using Artificial Neural Networks”, J. Soft Comput. Civ. Eng., vol. 3, no. 1, pp. 47–62.
[3]Sathyapriya, S., Arumairaj, P.D., and Ranjini, D. (2017). “Prediction of Unconfined Compressive Strength of a Stabilised Expansive Clay Soil using ANN and Regression Analysis (SPSS)”, Asian J. Res. Soc. Sci. Humanit., vol. 7, no. 2, pp. 109-123.
[4]Rajeev Jain, Pradeep Kumar Jain, and Sudhir Singh Bhadauria (2010). “Computational approach to predict soil shear strength”, International Journal of Engineering Science and Technology, vol. 2, no. 8, pp. 3874–3885.
[5]Banu Ikizler, S., Mustafa Aytekin, Mustafa Vekli, and Fikret Kocabas (2010). “Prediction of swelling pressures of expansive soils using artificial neural networks”, Advances in Engineering Software, vol. 41, pp. 647–655.
[6]Fatima Zohra Merouane, and Sidi Mohamed Aissa Mamoune (2018). “Prediction of Swelling Parameters of Two Clayey Soils from Algeria Using Artificial Neural Networks”, Math. Model. Civ. Eng., vol. 14, no. 3, pp. 11–26.
[7]Yusuf Erzin, and Nazmi Gunes (2011). “The prediction of swell percent and swell pressure by using neural networks”, Mathematical and Computational Applications, vol. 16, no. 3, pp. 702–711.
[8]Ismehen Taleb Bahmed, Khelifa Harichane, Mohamed Ghrici, Bakhta Boukhatem, Redouane Rebouh, and Hamid Gadouri (2019). “Prediction of geotechnical properties of clayey soils stabilised with lime using artificial neural networks (ANNs)”, Int. J. Geotech. Eng., vol. 13, no. 2, pp. 191–203.
[9]Salahudeen, A.B., Ijimdiya, T.S., Eberemu, A.O., and Osinubi, K.J. (2018). “Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust”, Soft Comput. Civ. Eng., vol. 2, no. 3, pp. 50–71.
[10]Ruhul Amin Mozumder, and Aminul Islam Laskar (2015). “Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network”, Comput. Geotech., vol. 69, pp. 291–300.
[11]Fikret Kurnaz, T., Ugur Dagdeviren, Murat Yildiz, and Ozhan Ozkan (2016).” Prediction of compressibility parameters of the soils using artificial neural network”, Springerplus, vol. 5, no. 1, pp. 1-11.
[12]Negin Yousefpour, Zenon Medina-Cetina, Francisco G. Hernandez-Martinez, and Abir Al-Tabbaa (2021). “Stiffness and Strength of Stabilized Organic Soils - Part II / II : Parametric Analysis and Modeling with Machine Learning”, Geosciences, pp. 1–22.
[13]Venkata Ratnam U., and Nagendra Prasad, K. (2019). “Prediction of Compaction and Compressibility Characteristics of Compacted Soils”, International Journal of Applied Engineering Research, vol. 14, no. 3, pp. 621–632.
[14]Hamed Javdanian, and Saro Lee (2019). “Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach”, Eng. Comput., vol. 35, no. 1, pp. 191–199.
[15]Sayyed Mahdi Hejazi, Mohammad Sheikhzadeh, Sayyed Mahdi Abtahi, and Ali Zadhoush (2012). “A simple review of soil reinforcement by using natural and synthetic fibers”, Constr. Build. Mater., vol. 30, pp. 100–116.
[16]Park, H.I., and Kim, Y.T. (2011). “Prediction of strength of reinforced lightweight soil using an artificial neural network”, Eng. Comput. (Swansea, Wales), vol. 28, no. 5, pp. 600–615.
[17]Amir Hossein Alavi, Amir Hossein Gandomi, Ali Mollahassani, Ali Akbar Heshmati, and Azadeh Rashed (2010). “Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks”, J. Plant Nutr. Soil Sci., vol. 173, no. 3, pp. 368–379.
[18]Goutham D.R. and A.J.Krishnaiah (2020). “Improvement in Geotechnical Properties of Expansive Soil Using Various Stabilizers: A Review”, I. J. Engineering and Manufacturing, vol.5, pp.18-27.