scholarly journals MODELIZACIÓN DE LA RESISTENCIA A LA COMPRESIÓN DEL CONCRETO MEDIANTE REDES NEURONALES ARTIFICIALES

2017 ◽  
Vol 23 (2) ◽  
pp. 11
Author(s):  
Leoncio Luis Acuña Pinaud ◽  
Ana Victoria Torre Carrillo ◽  
Isabel Moromi Nakata ◽  
Pedro Celino Espinoza Haro ◽  
Francisco García Fernández
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Compression Strength ◽  
Production Control ◽  
Load Capacity ◽  
High Capacity ◽  
Physical And Mechanical Properties ◽  
Structural Element ◽  
Artificial Neural

El uso del concreto como elemento estructural va aumentando año tras año. Sin embargo, este producto requiere de unos estrictos controles de calidad sobre sus propiedades mecánicas para el uso como elemento estructural. Este tipo de control implica la existencia de equipos de ensayo con una capacidad de carga de hasta 3.000KN. Sería de gran utilidad para el control de producción la utilización de un método alternativo de gran fiabilidad, que permitiera conocer las propiedades mecánicas a partir de otras propiedades físicas y mecánicas más fáciles de obtener. La alta capacidad de las redes neuronales artificiales (ANN) para modelar los más diversos procesos industriales, las convierte en una herramienta de gran utilidad en el ámbito de la industria del concreto. En este estudio se ha desarrollado una red neuronal para obtener la resistencia a compresión del concreto y se ha modelado dicha propiedad a partir de la composición del concreto y de sus parámetros de fabricación. La red neuronal diseñada, un perceptrón multicapa, ha permitido obtener la resistencia a compresión del concreto con un coeficiente de correlación de 0,97. Esto demuestra la capacidad de las redes neuronales artificiales para obtener la resistencia a compresión del concreto. Palabras clave.-Concreto, Resistencia a compresión, Redes neuronales artificiales. ABSTRACTThe use of concrete as a structural element increases year by year. However, this product needs very stringent control of its mechanical properties in order to be uses as structural element. This type of control requires to have very large testing equipment with a load capacity of up to 3.000KN. Production control would benefit greatly from the use of a highly reliable alternative method that would enable the mechanical properties to be found through more easily obtained physical and mechanical properties. The high capacity of artificial neural networks (ANN) to model a broad range of industrial processes makes them a very useful instrument in the concrete industry. In this study, one neural network was developed to obtain the properties of compressive strength. This property was then modeled though the composition of concrete and manufacturing parameters. The network designed, a multilayer perceptron, allowed the compression strength to be obtained with a regression coefficient of 0,97. This demonstrates the effectiveness of ANN for obtaining the mechanical properties of compression strength of concrete. Keywords.-Concrete, Compression strength, Artificial neural networks.

Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

2013 ◽  
Vol 773-774 ◽  
pp. 268-274
Author(s):  
Amir Ghiami ◽  
Ramin Khamedi
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Total Elongation ◽  
Ultimate Tensile Stress ◽  
Dual Phase ◽  
Dual Phase Steels ◽  
Good Ability ◽  
Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

Artificial Neural Networks for the Prediction of Mechanical Properties of Soils

2021 ◽  
pp. 758-779
Author(s):  
Lusdali Castillo Delgado ◽  
Daniel Enrique Porta Maldonado ◽  
Juan J. Soria ◽  
Leopoldo Choque Flores
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties

scholarly journals Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5227
Author(s):  
David Merayo ◽  
Alvaro Rodríguez-Prieto ◽  
Ana María Camacho
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Aluminum Alloys ◽  
Food Packaging ◽  
Relevant Information ◽  
Plastic Behavior ◽  
Artificial Neural

In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

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