Abstract
Small strain shear modulus is one of the most important geotechnical parameters to characterize soil stiffness. In-situ stiffness of soils and rocks is much higher than was previously thought as finite element analysis have shown. Also, the stress-strain behaviour of those materials is non-linear in most cases with small strain levels. The commun approach for getting the small strain shear modulus is usually based on measure of seismic wave velocities. Nevertheless, for design purposes is very useful to derive that modulus from correlations with in-situ tests output parameters. In this view, the use of Neural Networks seems very appropriate as the complexity of the system keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test.
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Cruz, M., Santos, J.M., Cruz, N. (2013). Estimating the Maximum Shear Modulus with Neural Networks. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_71
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DOI: https://doi.org/10.1007/978-3-642-38577-3_71
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