## 1 Introduction
Comparing the prediction performance of GEP models with that utilizing empirical results and relations based on regression showed that the GEP model had more precision in estimating the collapse potential of soil. [PERSON] (2020) made use of the multivariate adaptive regression spline (MARS) method to present models for predicting the collapse potential of dense soils [50]. In this research study, a dataset containing 330 data points was utilized and the parameters primary moisture content, primary dry unit weight, and wetting pressure were taken as independent variables. Then, the performances of the MARS method and ANN were compared in terms of prediction precision, calculation time, and model interpretation. The results revealed that the MARS model, with R\({}^{2}\) values of 0.948 and 0.926, had better performance for training and testing sets, respectively. [PERSON] (2021) modelled the collapse potential of gypseous soils using linear and nonlinear regression methods [51]. In this work, 220 collected data points from various studies were used to develop the model. The developed models could predict the collapse potential in terms of gypsum content, initial moisture content, void ratio, liquid limit, plasticity index, total unit weight, and dry unit weight. The results showed that the developed models, based on the statistical parameters of the regression coefficient (R\({}^{2}\)) and the root mean square error (RMSE), could predict the collapse potential of gypseous soils well. As the research background indicates, two intelligent models, ANN and MGGP, have been developed to predict the collapse settlement and coefficient of stress release based on sand content (SC), normal stress (\(\sigma_{n}\)), shear stress level (SL), and relative density (Dr) variables as affecting parameters. The models developed based on the artificial neural network method have several disadvantages, such as the \"black box\" nature of the developed model, greater computational load, and over-fitting potential [52]. Furthermore, in order to implement artificial neural network models and make predictions, matrix calculations are needed to simulate the network, which cannot be undertaken with simple manual calculations. GP uses an evolutionary approach to determine the mathematical form of the model, but the values of the constant coefficients of the model are generated randomly as non-tuneable constants. Therefore, the values obtained for the constant coefficients are not necessarily the optimal values and the proper structures of the model can be affected by these inaccurate coefficients. Bloating may also occur when modellingwith GP. Bloating causes the model sentences to grow excessively without any significant improvement in the overall performance. Therefore, bloating can hinder the progress of the evolutionary process of GP [53; 54]. Evolutionary polynomial regression (EPR) is a hybrid machine-learning method that was developed to overcome some of the drawbacks of the GP method. EPR requires a small number of constants to build the final model, which helps reduce the potential for over-fitting, especially for small datasets. It uses the least squares method to estimate the values of the coefficients, which makes it possible to obtain a unique solution when the inverse problem is well-conditioned [53]. In this study, the EPR technique was used to obtain simple equations for predicting the collapse settlement and coefficient of stress release for sandy gravel soil. The developed equations have high accuracy and generalizability and, due to their simple form, they can be easily implemented in manual calculations. After evaluating the precision of each of the models, the optimal model in terms of precision and simplicity was selected, and sensitivity analysis was performed to recognize the degree of importance of each of the inputs in predicting the collapse settlement and coefficient of stress release. Furthermore, a parametric study was also undertaken to identify the effects of changes in each input parameter on the output parameters.