Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

1998 ◽  
Vol 13 (4) ◽  
pp. 255-264 ◽  
Author(s):  
Yasuo Chikata ◽  
Noboru Yasuda ◽  
Manabu Matsushima ◽  
Tameo Kobori
Keyword(s):  
Neural Networks ◽  
Inverse Analysis ◽  
Concrete Structures ◽  
Aesthetic Evaluation

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

2011 ◽  
Vol 301-303 ◽  
pp. 597-602 ◽  
Author(s):  
Naasson P. de Alcantara ◽  
Danilo C. Costa ◽  
Diego S. Guedes ◽  
Ricardo V. Sartori ◽  
Paulo S. S. Bastos
Keyword(s):  
Neural Networks ◽  
Concrete Structures ◽  
Concrete Surface ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Electromagnetic Devices ◽  
Electromagnetic Measurements ◽  
Steel Bars ◽  
Non Destructive ◽  
The Magnetic Field

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values ​​for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

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