Time and Frequency Approaches to Non Destructive Testing in Concrete Pillars Using Neural Networks

Authors:
Barbara Cannas;Sara Carcangiu;Francesca Cau;Alessandra Fanni;Augusto Montisci
Affiliations:
Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123
Venue:
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Year:
2008

Citing 2
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Reinforced concrete structural damage diagnosis by using artificial neural network

IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)

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Abstract

In this paper, Multi Layer Perceptron neural networks have been trained to identify the position of defects in concrete structures analyzed using an ultrasound technique. A diagnostic model obtained by means of Finite Elements techniques has been used to model the ultrasound transmission through a concrete pillar of specified size affected by defects in different positions. The obtained signals have been processed both in the time and frequency domains, in order to reduce data dimensionality and to compute suitable features. Results show good accuracy in the identification of the position of the faults.