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    Wyświetlanie 1-2 z 2
    Tytuł:
    A study of bearing capacity of skirted octagonal footings resting on different sands
    Autorzy:
    Thakur, A.
    Dutta, R.K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2175811.pdf
    Data publikacji:
    2021
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    bearing capacity
    sands
    octagonal footing
    singly skirted (SS)
    doubly skirted (DS)
    nośność
    piaski
    stopa fundamentowa ośmiokątna
    Opis:
    Purpose: After a thorough study of literature it is concluded that the studies related to unskirted/skirted octagonal footings on sand have not yet been investigated. Thus, this paper presents a numerical analysis to assess the ultimate bearing capacity of the unskirted, unskirted-embedded, singly and doubly skirted octagonal footings resting on different sands (S1, S2, and S3). The length of skirt and depth of the embedded footing were varied from 0.0B to 1.5B. Design/methodology/approach: The numerical square and octagonal footing with singly and doubly skirted footing models were developed using Plaxis 3D software. Findings: The results of the doubly skirted octagonal footings ultimate bearing capacity were marginally higher in comparison to the singly skirted footing at all normalised skirt depths as well as for all sands up to a Ds/B ratio 0.25 beyond which the increase in the ultimate bearing capacity in case of doubly skirted footing was appreciable. Research limitations/implications: The results presented in this paper were based on numerical analysis. However, for the actual footings the soil placement and compaction, details of skirt construction and the stress level will be different from the numerical analysis. Further investigations using full-scale numerical models simulating field size footings were recommended to generalize the results. Originality/value: No such study on singly and doubly skirted octagonal shaped footings were conducted so far. Hence, an attempt was made in this article to predict the bearing capacity of those footings using Plaxis 3D.
    Źródło:
    Archives of Materials Science and Engineering; 2021, 107, 1; 21--31
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of flexural strength of FRC pavements by soft computing techniques
    Autorzy:
    Kimteta, A.
    Thakur, M.S.
    Sihag, P.
    Upadhya, A.
    Sharma, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200582.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    flexural strength
    fibre reinforced concrete
    artificial neural network
    random forest
    random tree
    M5P based model
    wytrzymałość na zginanie
    beton zbrojony włóknami
    sztuczna sieć neuronowa
    las losowy
    drzewo losowe
    model oparty na M5P
    Opis:
    Purpose: The mechanical characteristics of concrete used in rigid pavements can be improved by using fibre-reinforced concrete. The purpose of the study was to predict the flexural strength of the fibre-reinforced concrete for ten input variables i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer/high range water reducer, glass fibre, polypropylene fibre, steel fibres, length and diameter of fibre and further to perform the sensitivity analysis to determine the most sensitive input variable which affects the flexural strength of the said fibre-reinforced concrete. Design/methodology/approach: The data used in the study was acquired from the published literature to create the soft computing modes. Four soft computing techniques i.e., Artificial neural networks (ANN), Random forests (RF), Random trees RT), and M5P, were applied to predict the flexural strength of fibre-reinforced concrete for rigid pavement using ten significant input variables as stated in the ‘purpose’. The most performing algorithm was determined after evaluating the applied models on the threshold of five statistical indices, i.e., the coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error. The sensitivity analysis for most sensitive input variable was performed with out-performing model, i.e., ANN. Findings: The testing stage findings show that the Artificial neural networks model outperformed other applicable models, having the highest coefficient of correlation (0.9408), the lowest mean absolute error (0.8292), and the lowest root mean squared error (1.1285). Furthermore, the sensitivity analysis was performed using the artificial neural networks model. The results demonstrate that polypropylene fibre-reinforced concrete significantly influences the prediction of the flexural strength of fibre-reinforced concrete. Research limitations/implications: Large datasets may enhance machine learning technique performance. Originality/value: The article's novelty is that the most suitable model amongst the four applied techniques has been identified, which gives far better accuracy in predicting flexural strength.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 117, 1; 13--24
    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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