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    Wyświetlanie 1-2 z 2
    Tytuł:
    Sustainable deployment of crushed concrete aggregates strengthened with cement and sand
    Autorzy:
    Sharma, V.
    Kumar, A.
    Kaur, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/2201120.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    sustainability
    crushed concrete aggregate
    cement
    sand
    compaction
    CBR
    multiple regression
    zrównoważony rozwój
    kruszywo betonowe
    piasek
    zagęszczanie
    regresja wielokrotna
    Opis:
    Purpose: Paper assessed the feasibility of crushed concrete aggregates (CCA), a subsidiary of construction and demolition (C&D) waste, blended with cement and sand to form a composite for civil engineering field applications. Design/methodology/approach: The compaction and strength characteristics of CCA were observed by conducting Proctor compaction and California Bearing Ratio (CBR) tests. Different proportions of CCA, sand and cement were used. Moreover, the effect of curing period (0, 4, 7, 14 and 28 days) was also studied. In addition, regression analyses were performed to develop empirical expressions to predict the compaction and strength characteristics of the CCA composite. Findings: Increasing the CCA content up to 50% increases the maximum dry unit weight (MDUW) and decreases the optimum moisture content (OMC). However, on further increasing its content the MDUW decreases and OMC increases. Percent increase in the CBR value can go up to 412% if the CCA content is increased up to 50%. However, the percent reduction in CBR of about 20% can take place if 100% CCA content is used. Moreover, multiple regression shows that the experimental results are in good agreement with the predicted values. Research limitations/implications: The results obtained are purely dependent on the type of material. However, they are in favour of the used material as a probable option for road sub-base layer, and also for reducing burden on available natural resources. Therefore, it is recommended to conduct some initial tests to confirm the feasibility of the material. Practical implications: The proposed study will guide the design Engineers to choose CCA as one of the potential materials for road construction. Originality/value: It was observed that there is a need to maximize the utilization of C&D waste without making any compromise with its mechanical properties. So keeping that in view, the present study was conducted.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 113, 1; 19--34
    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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