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    Wyświetlanie 1-3 z 3
    Tytuł:
    A review: sustainable compressive strength properties of concrete mix with replacement by marble powder
    Autorzy:
    Sharma, N.
    Singh Thakur, M.
    Goel, P. L.
    Sihag, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/367956.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    marble powder
    strength properties
    cement
    aggregates
    proszek marmurowy
    właściwości wytrzymałościowe
    kruszywa
    Opis:
    Purpose: Over the years, various experiments have been performed to investigate the impact of marble powder within the concrete mixture. In the present study, a review has been done to check the persistence of marble dirt as the substitute for concrete constituents. Design/methodology/approach: Furthermore, the impact of marble powder as a replacement of cement and aggregates were reviewed. By reviewing previous studies, the result indicates that the use of waste marble powder in cement and aggregate was adequate to a certain range. Findings: By replacing cement with marble powder in a range between 5% to 10% by weight, it increases the compressive strength of concrete mix by 11.30% to 24.56%, compared to the nominal mix. According to the study, any further increase in the amount of marble powder in place of cement i.e, 12.5% to 20% replacement by weight, results in the reduction of compressive strength of concrete mix by 7.5% to 26.01%. Replacement of aggregates from 5% to 75% with marble powder increases the compressive strength of about 3.22% to 23.91% as compared to the nominal mix. Research limitations/implications: It was also concluded from the current study that, to obtain higher compressive strength, it is advantageous to replace fine aggregates with marble powder than the replacement of cement with the marble powder.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2020, 98, 1; 11-23
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing 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
    Tytuł:
    Comparative study for deriving stagedischarge – sediment concentration relationships using soft computing techniques
    Autorzy:
    Sihag, P.
    Sadikhani, M. R.
    Vambol, V.
    Vambol, S.
    Prabhakar, A. K.
    Sharma, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818806.pdf
    Data publikacji:
    2021
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    sediment load concentration
    Baitarani river
    M5P
    random forest
    ładunek osadu
    stężenie
    rzeka Baitarani
    las losowy
    Opis:
    Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2021, 104, 2; 57--76
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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