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Machine learning

The in-situ small-strain shear modulus of soil and rock materials is a parameter of paramount importance in geotechnical modeling. It can be derived from non-invasive geophysical surveys, which provide the possibility of testing the subsurface in its natural and undisturbed condition by inferring the velocity of propagation of shear waves. In addition, ...

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This paper explores the potential of machine learning techniques to predict the soil water retention curve based on physical characterization parameters. Results from 794 water retention and suction points obtained from 51 different soils were used in the algorithm. The soil properties used are the percentages of gravel, sand, silt, and clay, the plasti...

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Geotechnical engineers frequently rely on semi-empirical methods like Décourt-Quaresma and Meyehof’s to estimate the bearing capacity of piles. This paper proposes alternatives to these methods, presenting an approach using machine learning models for predicting the bearing capacity of precast concrete piles. It uses data samples in...

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Most work from the literature dedicated to soil classification systems from cone penetration test (CPT) data are based on simple two-dimensional charts. One alternative approach is using machine learning (ML) to produce new soil classification systems or to reproduce existing ones. The available studies within this research field can be considered limited, once ...
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