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    Wyświetlanie 1-2 z 2
    Tytuł:
    Soft computing based prediction of friction angle of clay
    Autorzy:
    Dutta, R. K.
    Gnananandarao, T.
    Ladol, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818506.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    artificial neural network
    sensitivity analysis
    M5P model tree
    multiregression analysis
    friction angle of clay
    sztuczna sieć neuronowa
    analiza wrażliwości
    drzewo modelu M5P
    analiza wielokrotnej regresji
    Opis:
    Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.
    Źródło:
    Archives of Materials Science and Engineering; 2020, 104, 2; 58--68
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep neural network and ANN ensemble for slope stability prediction
    Autorzy:
    Gupta, A.
    Aggarwal, Y.
    Aggarwal, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200566.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    upper clay
    lower clay
    peat
    angle of internal friction
    embankment
    factor of safety
    slope stability
    deep neural network
    ensemble
    glina górna
    glina dolna
    torf
    kąt tarcia wewnętrznego
    nasyp
    współczynnik bezpieczeństwa
    stabilność zbocza
    głęboka sieć neuronowa
    zespół
    Opis:
    Purpose: Application of deep neural networks (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability with a comparative performance analysis done for all techniques. Design/methodology/approach: 1000 cases with different geotechnical and similar Geometrical properties were collected and analysed using the Limit Equilibrium based Morgenstern-Price Method with input variables as the strength parameters of the soil layers, i.e., Su (Upper Clay), Su (Lower Clay), Su (Peat), angle of internal friction (φ), Su (Embankment) with the factor of safety (FOS) as output. The evaluation and comparison of the performance of predicted models with cross-validation having ten folds were made based on correlation-coefficient (CC), Nash-Sutcliffe-model efficiency-coefficient (NSE), root-mean-square-error (RMSE), mean-absolute-error (MAE) and scattering-index (S.I.). Sensitivity analysis was conducted for the effects of input variables on FOS of soil stability based on their importance. Findings: The results showed that these techniques have great capability and reflect that the proposed model by DNN can enhance performance of the model, surpassing ensemble in prediction. The Sensitivity analysis outcome demonstrated that Su (Lower Clay) significantly affected the factor of safety (FOS), trailed by Su (Peat). Research limitations/implications: This paper sets sight on use of deep neural network (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability. The current approach helps to understand the tangled relationship of various inputs to estimate the factor of safety of soil stability using DNN and ensemble of ANN with bagging. Practical implications: A dependable prediction tool is provided, which suggests that model can help scientists and engineers optimise FOS of soil stability. Originality/value: Recently, DNN and ensemble of ANN with bagging have been used in various civil engineering problems as reported by several studies and has also been observed to be outperforming the current prevalent modelling techniques. DNN can signify extremely changing and intricate high-dimensional functions in correlation to conventional neural networks. But on a detailed literature review, the application of these techniques to estimate factor of safety of soil stability has not been observed.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 116, 1; 14--27
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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