Discussion of “Prediction of the undrained shear strength of remolded soil with non-linear regression, fuzzy logic, and artificial neural network” [J. Mt. Sci. 21(9): 3108–3122]

This opinion article discusses the original research work of Yünkül et al. (the Authors) published in the Journal of Mountain Science 21(9): 3108–3122. Employing non-linear regression, fuzzy logic and artificial neural network modeling techniques, the Authors interrogated a large database assembled from the existing research literature to assess the performance of twelve equation rules in predicting the undrained shear strength (s u) mobilized for remolded fine-grained soils at different values of liquidity index (I L) and water content ratio. Based on their analyses, the Authors proposed a simple and reportedly reliable correlation (i.e., Eq. 9 in their paper) for predicting s u over the I L range of 0.15 to 3.00. This article describes various shortcomings in the Authors’ assembled database (including potentially anomalous data and covering an excessively wide I L range in relation to routine geotechnical and transportation engineering applications) and their proposed s u = f(I L) correlation. Contrary to the Authors’ assertions, their proposed correlation is not reliable for fine-grained soils with consistencies in the general firm to stiff range (i.e., for 0.15 < I L < 0.40), increasingly overestimating s u for reducing I L , and eventually predicting s u → +∞ for I L → 0.15+ (while producing mathematically undefined s u for I L < 0.15), thus rendering their correlation unconservative and potentially leading to unsafe geotechnical designs. Exponential or regular-power type s u = f(I L) models are more suitable when developing correlations that are applicable over the full plastic range (of 0 < I L < 1), thereby providing reasonably conservative s u predictions for use in the preliminary design for routine geotechnical engineering applications.