Steel bars identification in reinforced concrete structures by using ANN and magnetic fields - De Alcantara, N. P. [UNESP]; Gasparini, M. E L [UNESP] (The Electromagnetics Academy, 2005)
This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.
Keywords: Backpropagation; Bars (metal); Building materials; Composite beams and girders; Concrete buildings; Concrete construction; Concrete testing; Electric fault location; Ketones; Magnetic field measurement; Magnetic fields; Nondestructive examination; Piers; Reinforced concrete; Steel; Steel testing; Artificial neural networks; Finite element programs; Magnetic inductions; Multilayered; Non destructive testing; Perceptron; Real systems; Reinforced concrete structures; Steel bars; Training algorithms; Neural networks