scholarly journals Modelling the Influence of Waste Rubber on Compressive Strength of Concrete by Artificial Neural Networks

Materials ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 561 ◽  
Author(s):  
Marijana Hadzima-Nyarko ◽  
Emmanuel Karlo Nyarko ◽  
Naida Ademović ◽  
Ivana Miličević ◽  
Tanja Kalman Šipoš
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Reliable Prediction ◽  
Network Modelling ◽  
Natural Aggregate ◽  
Waste Rubber ◽  
Compressive Strength Of Concrete ◽  
Enormous Number

One of the major causes of ecological and environmental problems comes from the enormous number of discarded waste tires, which is directly connected to the exponential growth of the world’s population. In this paper, previous works carried out on the effects of partial or full replacement of aggregate in concrete with waste rubber on some properties of concrete were investigated. A database containing 457 mixtures with partial or full replacement of natural aggregate with waste rubber in concrete provided by different researchers was formed. This database served as the basis for investigating the influence of partial or full replacement of natural aggregate with waste rubber in concrete on compressive strength. With the aid of the database, the possibility of achieving reliable prediction of the compressive strength of concrete with tire rubber is explored using neural network modelling.

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 Rationalising crystal nucleation of organic molecules in solution using artificial neural networks

CrystEngComm ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 449-461
Author(s):  
Timothy Hjorth ◽  
Michael Svärd ◽  
Åke C. Rasmuson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Organic Molecules ◽  
Crystal Nucleation ◽  
Network Modelling ◽  
Primary Nucleation ◽  
Artificial Neural ◽  
Artificial Neural Network Modelling

Artificial neural network modelling is used to analyse and predict primary nucleation based on various physicochemical solute and solvent parameters.

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.

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