Download Citation | On Oct 1, 2013, Sakshi Gupta published Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica | Find, read and cite all the
[email protected]Sakshi Gupta 28 studied the ANN applications to predict the compressive strength of concrete containing nano-silica; an ANN model with correlation coefficient of 0.8685 was developed. An ANN can provide logical predicted values of compressive strength for 28 day-old specimens
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
May 01, 2009 Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. 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
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
Artificial Neural networks (ANN) are widely used in civil engineering for the prediction of the performance of some engineering materials such as compressive strength and durability. However, currently, studies on SCC containing silica fume are very rare
The data used in the model is arranged in the format of seven input parameters that cover the contents of cement (kg/m3), sand (kg/m3), coarse aggregate (kg/m3), Nano-Silica (kg/m3) as partial replacement of cement, fineness of nano-silica (mm), water/powder ratio and super plasticizer dosage (kg/m3) and an output parameter that is 28-day compressive strength
Silica fume, Compressive strength, Artificial Neural Networks. In this paper two Artificial Neural Network (ANN) models were developed for predicting the compressive strength of concrete containing Slag and Silica fume, at the age of 7, 28, 90 and 180 days. ANN models were constructed, trained and tested using 164 available data gathered
Oct 01, 2021 Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves J. Cleaner Prod. , 202 ( 2018 ) , pp. 54 - 64 Article Download PDF View Record in Scopus Google Scholar
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
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
Dec 13, 2019 Gupta S (2013) Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civ Eng Archit 1(3):96–102. Google Scholar 18. Diab A, Elyamany H, Abd Elmoaty M, Shalan A (2014) Prediction of concrete compressive strength due to long term sulfate attack using neural network
artificial intelligence technique- on incorporation of nanoparticles in predicting concrete strength after burning have been reported [18]. In this study, a smart modeling system utilizing Artificial Neural Network (ANN) is developed for predicting concrete compressive strength after burning
Jun 25, 2014 In this article, by using experimental studies and artificial neural network has been tried to investigate the use of nano-silica as concrete admixture to reduce alkali-silica reaction. If there are reactive aggregates and alkali of cement with enough moisture in concrete, a gel will be formed. Then with high reactivity between alkali of cement and existence of silica in aggregates, this gel
Jul 14, 2021 To predict the compressive strength of the self-compacting high-strength concrete mixed with silica fume, fly ash, and blast furnace slag aggregates, Jamaldin et al. established a neural network model, based on which they obtained good predictions of the experimental results. At present, ANN is mainly used to predict the compressive strength of
Aug 06, 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 14
Mar 01, 2020 [17] Chithra, S., Senthil Kumar, S.R.R., Chinnaraju, K., Ashmita, F.A. (2016) “A Comparative Study on the Compressive Strength prediction models for High Performance Concrete containing nano silica and copper slag using regression
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