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An Evaluative Analysis of the Utilization of Artificial Neural Networks in Forecasting Soil Engineering Characteristics: An Overview Study

G. Muthumari

Abstract


The primary objective of utilizing synthetic neural networks was to emulate the problem-solving approach akin to the human brain. This approach of employing artificial neural networks gained extensive traction within the realm of geotechnical engineering, where the engineering properties of soil play a pivotal role in ensuring the stability of various engineering structures. The primary concern revolves around the strength and deformation characteristics exhibited by soil masses. The focus lies predominantly on attributes that gauge the responsive behavior of soils under varying conditions. This review paper presents a succinct overview of the applications of Artificial Neural Networks (ANNs) in accurately predicting crucial engineering properties of soil. These properties encompass essential factors such as optimal moisture content, maximum dry density, permeability, shear strength parameters, and unconfined compressive strength. The comprehensive assessment suggests that ANNs, employing diverse models, exhibit a remarkable degree of accuracy in forecasting the engineering properties of soil. The review also underscores the ANNs' capacity to effectively manage incomplete input data, rendering them notably advantageous in such scenarios. This study is poised to provide substantial assistance to researchers immersed in exploring the realm of ANN applications within the context of soil behavior analysis.

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References


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Alavi AH, Gandomi AH, Mollahassani A, et al. Modeling of maximum dry density and optimum

moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci. 2010; 173:

–79p.

Salahudeen AB, Ijimdiya TS, Eberemu AO, et al. Artificial neural networks prediction of

compaction characteristics of black cotton soil stabilized with cement kiln dust. J Soft Comput Civil

Eng. 2018; 2(3): 53–74p.

Gunaydin O. Estimation of soil compaction parameters by using statistical analyses and artificial

neural networks. Environ Geol. 2009; 57: 203–15p.

Suman S, Mahamaya M, Das SK. Prediction of maximum dry density and unconfined compressive

strength of cement stabilized soil using artificial intelligence techniques. Int J Geosynth Ground

Eng. 2016; 2: 1-11p.

Abdel-Rahman AH (2008) Predicting compaction of cohesionless soils using ANN. Ground Improv

:3–8

Tizpa P, Chenari RJ, Fard MK, et al. ANN prediction of some geotechnical properties of soil from

their index parameters. Arab J Geosci. 2015; 8: 2911–20p.

Das SK, Samui P, Sabat AK. Application of artificial intelligence to maximum dry density and

unconfined compressive strength of cement stabilized soil. Geotech Geol Eng. 2011; 29: 329–42p.

Goh TC, Kulhawy FH, Chua CG. Bayesian Neural Network Analysis of Undrained Side Resistance

of Drilled Shafts. J Geotech Geoenvironmental Eng. 2005; 131: 84–93p.

Das SK, Basudhar SK. Prediction of residual friction angle of clays using artificial neural network.

Eng Geol. 2008; 100: 142–5p.

Shahiri J, Ghasemi M. Utilization of soil stabilization with cement and copper slag as subgrade

materials in road embankment construction. Int J Transp Eng. 2017; 5: 45–58p.

Alavi AH, Gandomi AH, Mollahassani A, et al. Modeling of maximum dry density and optimum

moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci. 2010; 173:

–79p.

Salahudeen AB, Ijimdiya TS, Eberemu AO, et al. Artificial neural networks prediction of

compaction characteristics of black cotton soil stabilized with cement kiln dust. J Soft Comput Civil

Eng. 2018; 2(3): 53–74p.


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