Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks

2013 ◽  
Vol 38 ◽  
pp. 717-722 ◽  
Author(s):  
Adriana Trocoli Abdon Dantas ◽  
Mônica Batista Leite ◽  
Koji de Jesus Nagahama
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Construction And Demolition Waste ◽  
Demolition Waste ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

scholarly journals Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

scholarly journals Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks

2016 ◽  
Vol 38 (1) ◽  
pp. 65 ◽  
Author(s):  
José Fernando Moretti ◽  
Carlos Roberto Minussi ◽  
Jorge Luis Akasaki ◽  
Cesar Fabiano Fioriti ◽  
José Luis Pinheiro Melges ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Modulus Of Elasticity ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

scholarly journals Modeling the Effect of Crude Oil Impacted Sand on the Properties of Concrete Using Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
W. O. Ajagbe ◽  
A. A. Ganiyu ◽  
M. O. Owoyele ◽  
J. O. Labiran
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Crude Oil ◽  
Activation Function ◽  
Soil Samples ◽  
Testing Data ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Number Of Iterations

A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at 3, 7, 14, 28, 56, 84, and 168 days at different degrees of contamination of 2.5%, 5%, 10%, 15%, 20% and 25%. A total of 49 samples were used in the training, testing, and prediction phase of the modeling in the ratio 32 : 11 : 7. The TANH activation function was used and the maximum number of iterations was limited to 20,000 the model used a momentum of 0.6 and a learning rate of 0.031056. Twenty (20) different architectures were considered and the most suitable one was the 2-2-1. Statistical analysis of the output of the network was carried out and the correlation coefficient of the training and testing data is 0.9955712 and 0.980097. The result of the network has shown that the use of neural networks is effective in the prediction of the compressive strength of concrete made from crude oil impacted sand.

A Simple Model of Predicting the Degree of Hydration of Concrete Using Artificial Neural Networks

2010 ◽  
Vol 168-170 ◽  
pp. 412-417
Author(s):  
Deng Xiang Zhang ◽  
Wei Jun Yang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Hydration Degree ◽  
Cement Ratio ◽  
Degree Of Hydration ◽  
Ordinary Concrete ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
The Relationship

Prediction of degree of hydration of concrete is very important on research of crack-resistance capability and durability of the structure. This article studied the relationship between degree of hydration and strength of concrete based on a large number of references, the results show that the compressive strength of concrete is closely related with the degree of hydration, and the correlation function is a function of water-cement ratio and has nothing to do with the temperature. The hydration degree and compressive strength of ordinary concrete is linear correlation, and the prediction model of degree of hydration of concrete was proposed based on BP Artificial Neural Networks.

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