To increase the strength of concrete, researchers are proposing new methods of reinforcement, usually with metallic structures or nanofibers. A professor from RUDN University with Iranian colleagues found an easier way. Even from a conventional concrete mix, a more durable material can be obtained. The main thing is to choose the right proportions and conditions for hardening. The results are published in Buildings.
To make concrete more resistant to static and cyclic loads, it is supplemented with “reinforcement” – reinforcement or nanofibers. At the same time, it is still necessary to look for ways to reinforce concrete even without reinforcement. For example, it is necessary to repair old structures built of ordinary concrete. A RUDN professor, together with Iranian colleagues, conducted a series of experiments and created an artificial neural network to calculate how to make concrete stronger without new “ingredients”.
“Concrete is a composite material of small and large aggregates, which are bound together with a cementing mortar, and harden. To increase the static and cyclic resistance of buildings, civil engineers use reinforced concrete. Large structures such as dams and multi-storey car parks are made of reinforced concrete.However, there are still old conventional concrete structures in the world that need to be renovated.Therefore, finding practical and inexpensive ways to increase the strength conventional concrete is still an important task. Most of the research is outdated. Only a few researchers are using new methods, such as data mining, neural network algorithms, hybrid optimization methods and machine learning to assess the strength of ordinary concrete,” said Kazem Reza Kashyzadeh, a professor in the Department of Transportation at RUDN University.
The engineers calculated the optimal mixing parameters that make the concrete as strong as possible without the use of additional elements. The strength is affected by the shape and size of the filler particles – crushed stone, gravel or sand – and the temperature at which the solution solidifies. The best shape of filler particles is rounded. Angular fractions, on the contrary, reduce resistance. As the particle size increases, the resistance increases. And the temperature at which the solution hardens is best kept at 10 degrees C. In this way, it is possible to achieve a 30% increase in the strength of concrete.
For the simulation, RUDN engineers created an artificial neural network using the so-called backpropagation method. To train the neural network, the researchers conducted a series of experiments with different concrete samples. Part of the experimental data was left to test the resulting model.
“We have found that in conventional concrete, the appearance of the aggregates, their size and geometry, as well as the curing conditions, have a significant impact on the strength. We have experimentally studied the relationship between these parameters and obtained the best conditions to obtain concrete,” said Professor Kashyzadeh.
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Kazem Reza Kashyzadeh et al, Prediction of Compressive Strength of Concrete Using Backpropagation Neural Network Optimized by Genetic Algorithm and Response Surface Analysis Considering Appearance of Aggregates and Conditions hardening, Buildings (2022). DOI: 10.3390/buildings12040438
Provided by the Russian Foundation for Basic Research
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