Application of ordinal logistic and gene expression programming methods to predict the collapse sensitivity classes of loess soils, a case study: Golestan Province, northeastern Iran

The current study assesses the collapse sensitivity classes of loess soils using gene expression programming (GEP) and ordinal logistic regression (OLR). The crucial variable to forecast the possible development of loess caves in the Golestan Province (northeast of Iran) is the collapse sensitivity factor (Is). A database of 62 records, including the mechanical and physical characteristics of soils, was used. Oedometer tests were used to estimate the parameters of the collapse coefficient, the time needed for 90% settlement (T90%), and collapse sensitivity. The database includes 10 inputs (grain size, porosity, initial water content, precipitation, climatic data, liquid limit, calcium carbonate content, vegetation, and degree of soil saturation) and one output (collapse sensitivity classes). This is a complicated approach due to the complexity of setting up and performing such kinds of tests in the laboratory. The likelihood of soil classification ranks as severe, moderately severe, moderate, and small sensitivity was inspected using OLR and GEP. This study demonstrated that the OLR approach could effectively differentiate among more than 70% of distinct groups. Furthermore, experimental data reported from Semnan, Sarakhs, and Mashhad also attests to the accuracy of the OLR model. The sensitivity analysis indicated that silt fraction imparts the maximum effect on the collapse sensitivity classes. The trial-and-error method was used to determine the configurations of the GEP model prior to developing an ideal model. The performance of the GEP model to estimate the collapse sensitivity categories in a trustworthy, strong, and useful way is well documented by comparison between the results of the GEP and the experimental findings, which are affordable.