Neural network predictions of the simulated rheological response of cement paste in the FlowCyl

2021 ◽  
Author(s):  
Shohreh Sheiati ◽  
Navid Ranjbar ◽  
Jes Frellsen ◽  
Elisabeth L. Skare ◽  
Rolands Cepuritis ◽  
...  
Keyword(s):  
Neural Network ◽  
Cement Paste ◽  
Rheological Response

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

scholarly journals Multiple Analytical Models to Evaluate the Impact of Carbon Nanotubes on the Electrical Resistivity and Compressive Strength of the Cement Paste

2021 ◽  
Vol 13 (22) ◽  
pp. 12544
Author(s):  
Nzar Shakr Piro ◽  
Ahmed Salih Mohammed ◽  
Samir Mustafa Hamad
Keyword(s):  
Neural Network ◽  
Carbon Nanotubes ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Electrical Resistivity ◽  
Carbon Nanotube ◽  
Regression Model ◽  
Cement Paste ◽  
Correlation Model ◽  
Data Points

Cement paste is the most common construction material being used in the construction industry. Nanomaterials are the hottest topic worldwide, which affect the mechanical properties of construction materials such as cement paste. Cement pastes containing carbon nanotubes (CNTs) are piezoresistive intelligent materials. The electrical resistivity of cementitious composites varies with the stress conditions under static and dynamic loads as carbon nanotubes are added to the cement paste. In cement paste, electrical resistivity is one of the most critical criteria for structural health control. Therefore, it is essential to develop a reliable mathematical model for predicting electrical resistivity. In this study, four different models—including the nonlinear regression model (NLR), linear regression model (LR), multilinear regression model (MLR), and artificial neural network model (ANN)—were proposed to predict the electrical resistivity of cement paste modified with carbon nanotube. Furthermore, the correlation between the compressive strength of cement paste and the electrical resistivity model has also been proposed in this study and compared with models in the literature. In this respect, 116 data points were gathered and examined to develop the models, and 56 data points were collected for the proposed correlation model. Most critical parameters influencing the electrical resistivity of cement paste were considered during the modeling process—i.e., water to cement ratio ranged from 0.2 to 0.485, carbon nanotube percentage varied from 0 to 1.5%, and curing time ranged from 1 to 180 days. The electrical resistivity of cement paste with a very large number ranging from 0.798–1252.23 Ω.m was reported in this study. Furthermore, various statistical assessments such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and OBJ were used to investigate the performance of different models. Based on statistical assessments—such as SI, OBJ, and R2—the output results concluded that the artificial neural network ANN model performed better at predicting electrical resistivity for cement paste than the LR, NLR, and MLR models. In addition, the proposed correlation model gives better performance based on R2, RMSE, MAE, and SI for predicting compressive strength as a function of electrical resistivity compared to the models proposed in the literature.

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

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

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
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