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.

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.

Application of data assimilation to ocean and climate prediction

Ocean prediction systems are now able to analyse and predict temperature, salinity and velocity structures within the ocean by assimilating measurements of the ocean’s temperature and salinity into physically based ocean models. Data assimilation combines current estimates of state variables, such as temperature and salinity, from a computational model with measurements of the ocean and atmosphere in order to improve forecasts and reduce uncertainty in the forecast accuracy. Data assimilation generally works well with ocean models away from the equator but has been found to induce vigorous and unrealistic overturning circulations near the equator. A pressure correction method was developed at the University of Reading and the Met Office to control these circulations using ideas from control theory and an understanding of equatorial dynamics. The method has been used for the last 10 years in seasonal forecasting and ocean prediction systems at the Met Office and European Center for Medium-range Weather Forecasting (ECMWF). It has been an important element in recent re-analyses of the ocean heat uptake that mitigates climate change.

O ensino de matemática na escola Industrial de Cuiabá/MT no período de 1942 e 1968

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Ideologia industrial x cultura rural: o método SESI de ensino em uma escola rural

This study compares two educational practices: the Rural School method (Escola do Campo) and the SESI teaching method, suggesting that the latter one is inefficient when applied to rural schools, as illustrated with a case study of a rural school that was obliged to adopt this method in 2012. The epistemological basis of a dialogical pedagogy for rural education has been used in order to criticize the practices of a method whose origins in the industrial ideology and in consumerism promotes a true cultural invasion, according to Paulo Freire, hindering the students' dialogues with respect to the ways of life in rural areas and in towns – an interaction that assured school performance in the previous educational system, which has been arbitrarily discontinued by the political power. Different surveys were used in this study for both compared cases, specially dissertations that have evaluated the Rural School project (Projeto Escola do Campo), adopted in Araraquara in 2004, a dissertation about the SESI teaching method that has discussed its new didactic material and, also, an evaluation of the contents of a representative sample of textbooks of History, Geography, Sciences and Mathematics for the 6th grade of elementary school. It is a theoretical text, not an essay, considering that it is based on concrete situations, which were explained using researches on the implicit themes and summarizes the analytical procedures that have allowed to unveil, in the textbooks prepared by SESI, the stimulus and the valorization of consumerism, without any criticism and environment concerns.

Final report on a survey of training and research in applied mathematics in the United States,

Mode of access: Internet.

Homo-motor, ciborgues e... aha! pessoas : da revolução industrial a revolução da informação

Universidade Estadual de Campinas . Faculdade de Educação Física

Saude coletiva e aptdão fisica de escolares de segundo grau : estudo a partis do Colegio Tecnico Industrial - UNESP, Bauru

Universidade Estadual de Campinas . Faculdade de Educação Física