ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING

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Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods (2012, background): “This is one of the most important and difficult tasks, since there is no unified theory for the optimal architecture (Shahin et al., 2001; Güllü and Erçelebi, 2008, 2007).”
Application of deep learning algorithms in geotechnical engineering: a short critical review (2021, background): “…of multi-layer neural network application are listed selectively in this article, as for the systematic review of ANN, it was discussed by numerous research scholars in the area of geotechnical engineering (Fatehnia and Amirinia 2018; Moayedi et al. 2020; Shahin 2015, 2016; Shahin et al. 2001).”
Machine learning in geosciences and remote sensing (2016, methodology): “…such as random forests, case-based reasoning, neuro-fuzzy (NF), genetic algorithm (GA), multi-variate adaptive regression splines (MARS), etc (e.g., Shahin et al., 2001; Shahin and Jaksa, 2005; Das and Basudhar, 2008; Samui, 2008a,b; 2012; Azamathulla and Wu, 2011; Azamathulla et al., 2011,…”
A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey (2007, methodology): “Even though many literatures suggest some values about the number of hidden layer and neuron, a trial and error procedure is generally used in geosciences (Shahin et al., 2001).”