scholarly journals Rheology modelling of cement paste with manufactured sand and silica fume: Comparing suspension models with artificial neural network predictions

2022 ◽  
Vol 317 ◽  
pp. 126114
Author(s):  
Elisabeth Leite Skare ◽  
Shohreh Sheiati ◽  
Rolands Cepuritis ◽  
Ernst Mørtsell ◽  
Sverre Smeplass ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silica Fume ◽  
Cement Paste ◽  
Manufactured Sand ◽  
Artificial Neural ◽  
Suspension Models

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

2009 ◽  
Vol 40 (9) ◽  
pp. 856-863 ◽  
Author(s):  
Fatih Özcan ◽  
Cengiz D. Atiş ◽  
Okan Karahan ◽  
Erdal Uncuoğlu ◽  
Harun Tanyildizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Fuzzy Logic ◽  
Silica Fume ◽  
Logic Models ◽  
Artificial Neural

Modelling the Yield Stress of Fly-Ash Added Superplasticized Cement Paste at Different Temperatures Using Artificial Neural Network

2022 ◽  
Vol 1048 ◽  
pp. 366-375
Author(s):  
Pavan Chandrasekar ◽  
Anjala Nourin ◽  
Addepalli Sri Naga Bhushana Aravind Gupta ◽  
Bavineni Venkata Jyoshna ◽  
Dhanya Sathyan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fly Ash ◽  
Yield Stress ◽  
Cement Paste ◽  
Test Temperature ◽  
Fresh Concrete ◽  
Rheological Parameters ◽  
Different Temperatures ◽  
Artificial Neural

Abstract: Rheology is the science that concerns the flow of liquids, and the distortion of solids under an applied force. The study of the rheology of concrete determines the properties of fresh concrete. The rheological parameters are affected by temperature, stress conditions and several other factors. The main intention of this research is to model the rheological parameters of the fly ash incorporated cement with various types of superplasticizers exposed under different temperatures using an Artificial Neural Network. Test data were generated by performing rheological tests on cement paste at three distinct temperatures (15, 27, 35°C). Mixes were prepared using OPC, fly ash (15, 25, 35%) and superplasticizers of four different families. By conducting experiments, 252 data have been generated by modifying the combination of fly-ash, superplasticizer, and test temperature. Among the 252 data, 80% has been utilized for training and 20% is utilized for predicting the model’s accuracy. The input layer of the model consists of test temperature, the amount of fly ash replaced, cement and water content, and four different groups of superplasticizers. The cement paste’s yield stress was the output parameter of the model. The model generated data has been compared with the experimentally generated data to determine the accuracy of the model.Keywords: Rheology, Fly Ash, Superplasticizer, Temperature, ANN

Modeling the effects of additives on rheological properties of fresh self-consolidating cement paste using artificial neural network

2011 ◽  
Vol 8 (3) ◽  
pp. 279-292 ◽  
Author(s):  
Alireze Mohebbi ◽  
Mohammad Shekarchi ◽  
Mehrdad Mahoutian ◽  
Shima Mohebbi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rheological Properties ◽  
Cement Paste ◽  
Artificial Neural

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

2021 ◽  
Vol 267 ◽  
pp. 121047 ◽  
Author(s):  
V. Soltangharaei ◽  
R. Anay ◽  
L. Assi ◽  
M. Bayat ◽  
J.R. Rose ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Cement Paste ◽  
Emission Data ◽  
Cracking Modes ◽  
Artificial Neural

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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