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Artificial neural network to predict the compressive strength of concrete containing nano silica

Gupta, S. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica. Civ. Eng. Archit. 2013, 1, 96–102. [Google Scholar] EN 197-1:2011 Cement. Composition, Specifications and Conformity Criteria for Common Cements

  • Applicability of Artificial Neural Networks to Predict
    Applicability of Artificial Neural Networks to Predict

    S. Chithra, S. R. R. Senthil Kumar, K. Chimaraju, and F. Alfin Ashmita, “A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks,” Construction and Building Materials, vol. 114, pp. 528–535, 2016

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  • A Novel Artificial Neural Network to Predict Compressive
    A Novel Artificial Neural Network to Predict Compressive

    Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it dif

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  • Application of artificial neural networks and multiple
    Application of artificial neural networks and multiple

    Jul 23, 2021 Sakshi Gupta 28 studied the ANN applications to predict the compressive strength of concrete containing nano-silica; an ANN model with correlation coefficient of

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  • Machine learning techniques to predict the compressive
    Machine learning techniques to predict the compressive

    The application of ANN in predicting the compressive strength of concrete containing nano-silica and copper slag was also addressed by Chithra et al. [14], showing promising results. Machine learning techniques such as ANN and SVM were used [15] and least-square SVM was improved using the metaheuristic optimization to predict the compressive

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  • ANN models for nano silica/ silica fume concrete
    ANN models for nano silica/ silica fume concrete

    (A) Use the Artificial Neural Networks in crea-tion probabilistic models for the expectation of the compressive strength of concrete with silica fume and nano silica. (B)Verification of the performance of each model. Data collection A total of 488concrete mixes were collected from 24 papers focused on studying the effect of NSand SF on

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  • Predicting the Compressive Strength of Desert Sand
    Predicting the Compressive Strength of Desert Sand

    Dec 17, 2020 S. Chithra, S. R. R. S. Kumar, K. Chinnaraju, and F. Alfin Ashmita, “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks,” Construction and Building Materials, vol. 114, pp. 528–535, 2016

    Get Price
  • Prediction of Concrete Properties Using Multiple
    Prediction of Concrete Properties Using Multiple

    Some studies for concrete containing various combinations of materials such as nano-silica and copper slag have been carried out [2]. One of the traditional methods used to predict compressive strength is Multiple Linear Regression (MLR) [3]. In recent past, the soft computing tool such as Artificial Neural Network (ANN) was employed to solve

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  • Predicting the strength properties of slurry infiltrated
    Predicting the strength properties of slurry infiltrated

    Dec 19, 2017 Gupta S. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica. Civil Engineering and Architecture, 2013, 1: 96–102. Google Scholar 27. Hush D R, Horne B G. Progress in supervised Neural Network: What is New since Lippman. IEEE Signal Processing Magazine, 1993, 10: 8–39

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  • Predicting the compressive strength of silica fume
    Predicting the compressive strength of silica fume

    Nov 20, 2018 Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete Adv. Eng. Softw. , 40 ( 9 ) ( 2009 ) , pp. 856 - 863 Article Download PDF View Record in Scopus Google Scholar

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  • Experimental Study and Prediction Model of the Flexural
    Experimental Study and Prediction Model of the Flexural

    S. Chithra, S. R. R. Senthil Kumar, K. Chinnaraju, and F. Alfin Ashmita, “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks,” Construction and Building Materials, vol. 114, pp. 528–535, 2016

    Get Price
  • Artificial neural network model to predict the
    Artificial neural network model to predict the

    Jan 10, 2021 Moreover, this study proposed an Artificial Neural Network (ANN) to predict the compressive strength of pozzolanic GPC based on GGBS (i.e., at the ages of 7, 28, and 90 days). The compressive strength of GGBS-based GPC (i.e., 117 concrete specimens manufactured out of 39 various mixtures) obtained by experimental tests was used to develop the model

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  • Prediction of compressive strength of roller compacted
    Prediction of compressive strength of roller compacted

    Jul 21, 2021 Chithra S, Kumar SS, Chinnaraju K, Ashmita FA (2016) A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks. Constr Build Mater 114:528–535. Article Google Scholar 5

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  • A comparative study on the compressive strength
    A comparative study on the compressive strength

    Jul 01, 2016 In this study, Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) models are constructed to predict the compressive strength of High Performance Concrete containing nano silica and copper slag as partial cement and fine aggregate replacement respectively

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  • [PDF] Prediction of compressive strength of SCC and HPC
    [PDF] Prediction of compressive strength of SCC and HPC

    An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity

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  • A Novel Artificial Neural Network to Predict
    A Novel Artificial Neural Network to Predict

    Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it dif

    Get Price
  • Compressive strength prediction of environmentally
    Compressive strength prediction of environmentally

    In this study, Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) models are constructed to predict the compressive strength of High Performance Concrete containing nano silica and copper slag as partial cement and fine aggregate replacement respectively

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  • Prediction of compressive strength of concretes
    Prediction of compressive strength of concretes

    May 01, 2009 In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180 days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture

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