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
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
Get PriceMost 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 PriceJul 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
Get PriceThe 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
Get Price(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
Get PriceDec 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 PriceSome 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
Get PriceDec 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
Get PriceNov 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
Get PriceS. 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 PriceJan 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
Get PriceJul 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
Get PriceJul 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
Get PriceAn 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
Get PriceMost 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 PriceIn 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
Get PriceMay 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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