A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural NetworkReport as inadecuate

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Advances in Artificial Neural SystemsVolume 2011 2011, Article ID 786535, 9 pages

Research ArticleDepartment of Civil and Structural Engineering, Annamalai University, Tamilnadu, Annamalainagar 608 002, India

Received 31 May 2011; Revised 24 August 2011; Accepted 24 August 2011

Academic Editor: Wilson Wang

Copyright © 2011 K. Sumangala and C. Antony Jeyasehar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A damage assessment procedure has been developed using artificial neural network ANN for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete PSC rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper.

Author: K. Sumangala and C. Antony Jeyasehar

Source: https://www.hindawi.com/


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