Application of Artificial Neural Networking Technique to Predict the Geotechnical Aspects of Expansive Soil: A Review

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Goutham D.R. 1,* A.J.Krishnaiah 1

1. Department of Civil Engineering, Malnad College of Engineering, Hassan-573202, affiliated to Visvesvaraya Technological University, India.

* Corresponding author.


Received: 15 Aug. 2021 / Revised: 2 Sep. 2021 / Accepted: 27 Sep. 2021 / Published: 8 Dec. 2021

Index Terms

Artificial neural networks, Expansive soils, Free swell index and Swelling pressure.


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


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