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

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@Article{Shahnazari:2013:CG,

• author = "Habib Shahnazari and Mohammad A. Tutunchian and Reza Rezvani and Fatemeh Valizadeh",
• title = "Evolutionary-based approaches for determining the deviatoric stress of calcareous sands",
• journal = "Computer \& Geosciences",
• volume = "50",
• month = jan,
• pages = "84--94",
• year = "2013",
• note = "Benchmark problems, datasets and methodologies for the computational geosciences",
• keywords = "genetic algorithms, genetic programming, Calcareous sands, Dataset, Modeling, Triaxial experiments",
• ISSN = "0098-3004",
• DOI = "doi:10.1016/j.cageo.2012.07.006",
• URL = "http://www.sciencedirect.com/science/article/pii/S0098300412002312",
• abstract = "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 behaviours 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 modelling 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.",

}

Genetic Programming entries for Habib Shahnazari Mohammad Amin Tutunchian Reza Rezvani Fatemeh Valizadeh

Citations