Investigating the Most Important Geomechanical Parameter of Unsaturated Soils using Different Artificial Intelligence Methods*

In predicting the behavior of geotechnical structures that are constructed on unsaturated soils, knowing the geomechanical properties, especially effective stress, is of particular importance. The effect of suction in unsaturated soils changes the volumetric and shear behavior of soil. Therefore, in determining the shear strength in unsaturated soils, the effective stress parameter (χ) plays an essential role as a function of suction. Determining this parameter requires a lot of time and money that is spent on laboratory tests. The purpose of this study is to evaluate several intelligent methods for creating models that indirectly accurately estimate parameter (χ). For this purpose, 120 data (which are the results of thriaxial, shear, pressure plate and filter paper tests) and intelligent methods of random forest, support vector machine and k-nearest neighbor were used in WEKA intelligent software. The results show that the models developed by the three intelligent methods of random forest, support vector machine and k-nearest neighbor, have excellent performance and accuracy. But the random forest model is better than the other two models in evaluating the test data with R2 = 0.918 and RMSE = 0.079. In addition, in this study, sensitivity analysis was performed to determine the importance of the parameters affecting parameter (χ). Among the input parameters in modeling, it was found that the volume water content parameter (θ_r/θ_s ) has the greatest effect on parameter (χ).