Transport properties of self-consolidating concrete of different mix design approachs

2005 ◽  
Author(s):  
S. Hwang
Keyword(s):  
Transport Properties ◽  
Mix Design ◽  
Self Consolidating Concrete

Mix design of the green self-consolidating concrete: Incorporating the waste glass powder

2019 ◽  
Vol 199 ◽  
pp. 369-384 ◽  
Author(s):  
Ali Hendi ◽  
Davood Mostofinejad ◽  
Arash Sedaghatdoost ◽  
Mehdi Zohrabi ◽  
Navid Naeimi ◽  
...  
Keyword(s):  
Glass Powder ◽  
Waste Glass ◽  
Mix Design ◽  
Self Consolidating Concrete ◽  
Waste Glass Powder

scholarly journals Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6792
Author(s):  
Jing Liu ◽  
Masoud Mohammadi ◽  
Yubao Zhan ◽  
Pengqiang Zheng ◽  
Maria Rashidi ◽  
...  
Keyword(s):  
Fly Ash ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Mix Design ◽  
Mean Square ◽  
Fresh Properties ◽  
Self Consolidating Concrete ◽  
Significant Parameter

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods.

Transport properties of nano-silica contained self-consolidating concrete

2021 ◽  
Vol 301 ◽  
pp. 124060
Author(s):  
Nader Ghafoori ◽  
Borhan Moradi ◽  
Meysam Najimi ◽  
Ariful Hasnat
Keyword(s):  
Transport Properties ◽  
Nano Silica ◽  
Self Consolidating Concrete

scholarly journals Valorisation of sediments in self-consolidating concrete: Mix-design and microstructure

2015 ◽  
Vol 81 ◽  
pp. 1-10 ◽  
Author(s):  
E. Rozière ◽  
M. Samara ◽  
A. Loukili ◽  
D. Damidot
Keyword(s):  
Mix Design ◽  
Self Consolidating Concrete ◽  
Concrete Mix Design ◽  
Concrete Mix

Effect of mix design on restrained shrinkage of self-consolidating concrete

2009 ◽  
Vol 43 (3) ◽  
pp. 367-380 ◽  
Author(s):  
Soo-Duck Hwang ◽  
Kamal H. Khayat
Keyword(s):  
Mix Design ◽  
Restrained Shrinkage ◽  
Self Consolidating Concrete

Transport Properties of Limestone-Containing Self-Consolidating Concrete

2017 ◽  
Vol 114 (4) ◽  
Author(s):  
Nader Ghafoori ◽  
Rebecca Spitek ◽  
Meysam Najimi
Keyword(s):  
Transport Properties ◽  
Self Consolidating Concrete
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