## 1 Introduction
However, higher collapse potential has been observed for samples that have higher matrix suction, regardless of their dry density and moisture percentage [24]. On the other hand, it has been observed that collapse potential for different soils increases as the porosity rate increases [26]. Previous studies have also showed that the primary percentage of moisture in soil has had the strongest effect on collapse settlement, as reduction in the primary percentage of moisture leads to collapse settlement. In order to investigate the stress and strain paths during collapse settlement, the use of collapse settlement (\(\Delta\)H) and coefficient of stress release (CSR) values has been proposed. [20]. The CSR is typically defined as the ratio of shear stress in saturated conditions (\(\tau_{c}\)) to the shear stress in dry conditions (\(\tau_{t}\)):  
\[CSR=\frac{\tau_{c}}{\tau_{t}} \tag{1}\]  
Therefore, the shear stress of soil after collapse can be determined by multiplying the CSR by the soil shear stress in dry conditions. Measuring \(\Delta\)H and CSR requires complicated laboratory tests and expensive equipment. Hence, it seems that developing predictive models could be an alternative to deal with such a complicated problem. Soft computing-based approaches have been effectively applied in order to model and predict mechanical behaviour and material strength in the field of civil engineering [27; 28; 29; 30; 31; 32; 33; 34; 35]. [PERSON] et al. [46] modelled the collapse settlement of sandy gravel via an artificial neural network (ANN) [46]. To this end, 180 data points obtained from a large-scale direct shear experiment were used as a dataset. Additionally, sand content, shear stress level, normal stress, and relative density were considered as independent variables,and the collapse settlement of sandy gravel soil and coefficient of stress release were selected as dependent parameters. The results of the study showed that the proposed model had the capability of predicting the collapse settlement of the soil and the coefficient of stress release with R\({}^{2}\) values of 0.9828 and 0.9806, respectively. [PERSON] et al. [47] offered a model based on multi-gene genetic programming (MGGP) and multi-variable least square regression (MLSR) to predict collapse settlement and the coefficient of stress release [47]. To develop the model, they used a dataset containing 180 experimental samples. The developed models could be used to estimate the collapse settlement and coefficient of stress release based on the sand content, shear stress level, normal stress, and relative density. The MGGP model could predict the collapse settlement and coefficient of stress release with R\({}^{2}\) values of 0.958 and 0.982, respectively. This model, compared with the MLSR model that predicted the collapse settlement and coefficient of stress release with R\({}^{2}\) values of 0.857 and 0.942, showed higher accuracy. [PERSON] et al. (2020) used the ANN method to predict the collapse potential of gypserous sandy soils [48]. Sandy soils were taken from four zones in Iraq to produce 180 samples with varying properties. This experimental study included estimation of the collapse potential with an oedometer device. Seven parameters of soil, including gypsum content, specific density, primary dry unit weight, primary saturation degree, primary porosity ratio, primary moisture content, and the passing percentage through a No. 200 sieve (0.074 mm), were regarded as input variables. Moreover, the collapse potential of gypserous sandy soils was taken as the output. The results revealed that the ANN method could desirably estimate the collapse potential. [PERSON] (2020) presented a model to predict the collapse potential of soil via gene expression programming (GEP) [49]. The dataset used in the study included uniformity coefficient, primary moisture content, primary dry unit weight, and wetting pressure.