The Experts below are selected from a list of 4152 Experts worldwide ranked by ideXlab platform

Gutstein Daniela - One of the best experts on this subject based on the ideXlab platform.

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application
    'Scipedia S.L.', 2019
    Co-Authors: Araujo, Leilson Joaquim, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.Peer Reviewe

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application.
    2019
    Co-Authors: Araujo Leilson, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented

Vieira Agno - One of the best experts on this subject based on the ideXlab platform.

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application
    'Scipedia S.L.', 2019
    Co-Authors: Araujo, Leilson Joaquim, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.Peer Reviewe

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application.
    2019
    Co-Authors: Araujo Leilson, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented

Pilar Queralt - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set (Germany)
    Surveys in Geophysics, 2013
    Co-Authors: David Bosch, Juanjo Ledo, Pilar Queralt
    Abstract:

    Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a Matlab Routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.

Araujo Leilson - One of the best experts on this subject based on the ideXlab platform.

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application.
    2019
    Co-Authors: Araujo Leilson, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented

Araujo, Leilson Joaquim - One of the best experts on this subject based on the ideXlab platform.

  • Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application
    'Scipedia S.L.', 2019
    Co-Authors: Araujo, Leilson Joaquim, Vieira Agno, Gutstein Daniela
    Abstract:

    Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab Routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.Peer Reviewe