Predicting the chemical and mechanical properties of gypseous soils using different simulation technics

Gypseous soils are soils that contain sufficient quantities of gypsum that are considered collapsible soil. The present study's objective is to predict the shear strength parameters ( c , ϕ ), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties using a wide range of 220 collected data from various published articles. The linear and nonlinear approaches were used in this study, and the outcomes of the models were compared with artificial neural network (ANN) performance. The developed models predicted the shear parameters, compression index, gypsum content, and collapse potential as a function of accessible laboratories measurable such as specific gravity, moisture content, density, and Atterberg limits with acceptable accuracy. The soils' gypsum content (Gc) was also correlated well based on the total soluble salts (TSS), sulfate (SO 3 ), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted ( R 2 ), mean absolute error (MAE), and the root-mean-square error (RMSE), the linear and nonlinear models predicted the shear strength parameters, compression index, and collapse potential of the gypseous soils very well. The regression model predictions were comparable to the outcomes from the ANN model predicting.