Machine Learning Estimation of the Unit Weight of Organic Soils

The aim of this study is to search for and verify regression models of selected geotechnical parameters of organic soils that are useful in engineering practices. Various machine learning methodologies were employed, including decision tree, ensembles of trees, support vector regression, Gaussian process, and neural networks. The work was based on two qualitatively different examples of estimating the unit weight of soil (γt). In the first example, the results of cone penetration test (CPT) probing (cone resistance qc and friction resistance fs) were used. In the second example, the results of laboratory tests of other physical properties of these soils (content of organic parts LOIT and moisture content w) were used. The task was completed for 135 sets of test results, which were carried out at the Rzeszów training ground in Poland with in situ tests using the CPT probe and laboratory tests. A statistical analysis was carried out to initially determine the relationships between the variables. This work presents the results of a comparison of multiple linear regression models with regression models obtained using the machine learning (ML) method. The studies obtained ML models with mean absolute percentage errors (MAPE) that were smaller than those of statistical models. Consequently, for the CPT sounding data, the MAPE changed from 13.57% to 7.37%, and, for the second data set, from 7.87% to 1.25%. Software STATISTICA version 13.3 and the Regression Learner TM library from MATLAB R2024b were used to analyze the soil data.