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