Citas bibligráficas
Vega, J., (2019). Modelamiento computacional de la resistencia a compresión y trabajabilidad del concreto ligero con esferas de poliestireno expandido [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/18207
Vega, J., Modelamiento computacional de la resistencia a compresión y trabajabilidad del concreto ligero con esferas de poliestireno expandido [Tesis]. : Universidad Nacional de Ingeniería; 2019. http://hdl.handle.net/20.500.14076/18207
@misc{renati/264437,
title = "Modelamiento computacional de la resistencia a compresión y trabajabilidad del concreto ligero con esferas de poliestireno expandido",
author = "Vega More, Juan Jesús",
publisher = "Universidad Nacional de Ingeniería",
year = "2019"
}
This research studies lightweight expanded polystyrene concrete reinforced, the goal is to determine and predict two important properties of the concrete in fresh and hardened state, such as compressive strength and workability; following an experimental method based in laboratory tests to get data. In this research, design is based in the modified Andreasen and Andersen particle packing model in order to achieve a densely compacted target gradation curve for all solid particles in the mixture. Altogether were designed 9 lightweight concrete mixes, with three EPS percentages (0%, 22.5%, 25% by volume) and water/binder ratio of 0.42, 0.38, 0.35 are considered. Once obtained values of compressive strength and workability of the samples, it is proceed to use computational tools, such as neural networks, support vector machine, which represent the behavior of this type of concrete, and to be able to make predictions with good percentage of reliability. To fit these mathematical models it collected data of the bibliography that have conditions similar to the sample tested. The main problem found is the difficulty to predict with good precision the properties of the concrete, specifically one with special characteristics, for which the methodology for the implementation of computational methods is presented, as well as two specific models mentioned above. This way you can contribute in the optimization of the mix designs and the saving of materials.
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