scholarly journals Prediction of Creep Strain for Self-Compacting Concrete by Artificial Neural Networks

2019 ◽  
Vol 10 (2) ◽  
pp. 90-100
Author(s):  
Ammar Al-Rihimy ◽  
◽  
Basil Al-Shathr ◽  
Tareq Al-Attar ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Creep Strain ◽  
Self Compacting Concrete ◽  
Artificial Neural

An Artificial Neural Networks Model for Compressive Strength of Self-Compacting Concrete

2016 ◽  
Vol 845 ◽  
pp. 226-230
Author(s):  
Akhmad Suryadi ◽  
Qomariah ◽  
M. Sarosa
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Experimental Tests ◽  
Experimental Program ◽  
Self Compacting Concrete ◽  
Design Program ◽  
Artificial Neural ◽  
Program Sample ◽  
Mathematic Program

An experimental program was undertaken to evaluate the compressive strength of self-compacting concrete using commercial mathematic program. Sample variation was monitored using an experimental cylinder of concrete measuring 150 mm in diameter and 300 mm in height. This research examined various mixture designs in the laboratory tests with the goal of creating mixtures with desirable flow specification that did not require additional vibration yet provided adequate compressive strength. After 28 days, compressive strength of cylinder concrete determination, a model of Artificial Neural Networks (ANNs) was designed for this research and the results were obtained in this model of ANN. Both experimental tests and mix design program data was analyzed with statistical packet software. The result of statistical analysis has been done in 98.54 percent of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar experiment.

scholarly journals Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

2021 ◽  
Vol 7 (1) ◽  
pp. 118-139
Author(s):  
Mahmoud Serraye ◽  
Said Kenai ◽  
Bakhta Boukhatem
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Silica Fume ◽  
Carbon Dioxide Emissions ◽  
Experimental Studies ◽  
Back Propagation Algorithm ◽  
Self Compacting Concrete ◽  
Cement Replacement ◽  
Artificial Neural

Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume. Cement replacement materials provide a wide variety of benefits such as lower cost, reduced consumption of natural resources, reduced carbon dioxide emissions and improved fresh and hardened properties. SCC is used in many applications such as sections with congested reinforcement and high rise shear walls and there is a need for the prediction of the performance of SCC used. 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. In this paper, an artificial neural networks (ANN) model is developed to predict the compressive strength of SCC with silica fume using the Levenberg-Marquardt back propagation algorithm based on a database from 366 experimental studies. The model developed was correlated with a nonlinear relationship between the constituents (input) and the compressive strength of SCC (output). To evaluate the predictive ability and generalize the developed model, other researchers’ experimental results were compared with the model prediction and good agreements are found. A parametric study was conducted to study the sensitivity of the ANN proposed model to some parameters such as water/binder ratio and superplasticizer content. The model developed in this study can potentially be used for SCC compressive strength prediction with very acceptable results and a high correlation coefficient R2=0.93. The developed model is practical, easy to use and user friendly. Doi: 10.28991/cej-2021-03091642 Full Text: PDF

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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