Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Characterization of an antioxidant surfactant-free topical formulation containing Castanea sativa leaf extract

Context: Inclusion of antioxidants in topical formulations can contribute to minimize oxidative stress in the skin, which has been associated with photoaging, several dermatosis and cancer. Objective: A Castanea sativa leaf extract with established antioxidant activity was incorporated into a semisolid surfactant-free formulation. The objective of this study was to perform a comprehensive characterization of this formulation. Materials and methods: Physical, microbiological and functional stability were evaluated during 6 months storage at 20 °C and 40 °C. Microstructure elucidation (cryo-SEM), in vitro release and in vivo moisturizing effect (Corneometer® CM 825) were also assessed. Results and discussion: Minor changes were observed in the textural and rheological properties of the formulation when stored at 20 °C for 6 months and the antioxidant activity of the plant extract remained constant throughout the storage period. Microbiological quality was confirmed at the end of the study. Under accelerated conditions, higher modifications of the evaluated parameters were observed. Cryo-SEM analysis revealed the presence of oil droplets dispersed into a gelified external phase. The release rate of the antioxidant compounds (610 ± 70 µgh−0.5) followed Higuchi model. A significant in vivo moisturizing effect was demonstrated, that lasted at least 4 h after product’s application. Conclusion: The physical, functional and microbiological stability of the antioxidant formulation was established. Specific storage conditions should be recommended considering the influence of temperature on the stability. A skin hydration effect and good skin tolerance were also found which suggests that this preparation can be useful in the prevention or treatment of oxidative stress-mediated dysfunctions.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.