Proposing new relationships to estimate the pressuremeter modulus of cohesive and cohesionless media

Pressuremeter is one of the most reliable geotechnical in situ tests to be utilized in estimating different soil properties. The main parameters that can be obtained from this test are soil deformation modulus. In general, the pressuremeter test is time-consuming and costly that requires suitable equipment and experienced operators. With these limitations, it is necessary to introduce models for indirect determination of the pressuremeter modulus (E_PM). Based on the literature, various models and equations have been proposed to predict the modulus of pressuremeter; however, most of them are for fine-grained soils. In this paper, multiple linear regression (MLR), artificial neural network (ANN), and gene expression programming (GEP) have been used to design new relationships for prediction of the pressuremeter modulus. The main difference between this research and other studies is the use of 62 pre-bored pressuremeter tests results in cohesive and granular soils for training and testing models. The study area is in Tehran where soils show a variation from low plasticity clay to clayey gravel. The moisture content, depth of test, and grain size distribution of soils are considered as independent variables. Comparison of the predicted E_PM with the actual modulus obtained from the pressuremeter tests indicates that the proposed relationships are able to estimate the pressuremeter modulus well. The results showed that the relationships obtained from nonlinear analyses performed by smart methods are more accurate and have less error in comparison to MLR method. Comparison of the coefficient of determination (R²) values and errors related to them in the testing phase show that the obtained results from the GEP method are more reliable than the ANN method. Also, sensitivity analysis revealed that the soil moisture content is the most effective parameter among the variables on the pressuremeter modulus.