Evolutionary-based approaches for determining the deviatoric stress of calcareous sands

Many hydrocarbon reservoirs are located near oceans which are covered by calcareous deposits. These sediments consist mainly of the remains of marine plants or animals, so calcareous soils can have a wide variety of engineering properties. Due to their local expansion and considerable differences from terrigenous soils, the evaluation of engineering behaviors of calcareous sediments has been a major concern for geotechnical engineers in recent years. Deviatoric stress is one of the most important parameters directly affecting important shearing characteristics of soils. In this study, a dataset of experimental triaxial tests was gathered from two sources. First, the data of previous experimental studies from the literature were gathered. Then, a series of triaxial tests was performed on calcareous sands of the Persian Gulf to develop the dataset. This work resulted in a large database of experimental results on the maximum deviatoric stress of different calcareous sands. To demonstrate the capabilities of evolutionary-based approaches in modeling the deviatoric stress of calcareous sands, two promising variants of genetic programming (GP), multigene genetic programming (MGP) and gene expression programming (GEP), were applied to propose new predictive models. The models’ input parameters were the physical and in-situ condition properties of soil and the output was the maximum deviatoric stress (i.e., the axial-deviator stress). The results of statistical analyses indicated the robustness of these models, and a parametric study was also conducted for further verification of the models, in which the resulting trends were consistent with the results of the experimental study. Finally, the proposed models were further simplified by applying a practical geotechnical correlation.

► We gathered a database of the maximum deviatoric stress of various calcareous sands.
► The database was used to develop new evolutionary-based predictive models.
► Statistical analyses showed the high precision of proposed models.
► The models can simply used for estimation of the maximum friction angle.