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 (SO₃), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted (R²), 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.

石膏土是含有足量石膏的土壤，被认为是可塌陷的土壤。本研究的目标是预测剪切强度参数 ( c , ϕ)、坍塌潜力 (CP) 和来自膏状土壤物理特性的压缩指数 (Cc) 使用从各种已发表文章中收集的 220 种广泛数据。本研究中使用了线性和非线性方法，并将模型的结果与人工神经网络 (ANN) 的性能进行了比较。开发的模型预测剪切参数、压缩指数、石膏含量和坍塌潜力，作为可访问实验室的函数，可测量，如比重、水分含量、密度和阿特伯格极限，精度可接受。使用非线性 Vipulanandan 相关模型，基于总可溶性盐 (TSS)、硫酸盐 (SO ₃ ) 和 pH 值，土壤的石膏含量 (Gc) 也具有良好的相关性。基于调整后的 ( R ²)、平均绝对误差(MAE)和均方根误差(RMSE)，线性和非线性模型很好地预测了膏质土的抗剪强度参数、压缩指数和坍塌潜力。回归模型预测与 ANN 模型预测的结果相当。