Computational & Technology Resources
an online resource for computational,
engineering & technology publications
Civil-Comp Proceedings
ISSN 1759-3433
CCP: 109
PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping
Paper 33

An Artificial Neural Network for Locating Possible Damaged Zones in Beam Structures

J.D. Villalba1,2 and J.E. Laier2

1Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia
2Deparment of Structural Engineering, University of Sao Paulo, Sao Carlos, Brazil

Full Bibliographic Reference for this paper
J.D. Villalba, J.E. Laier, "An Artificial Neural Network for Locating Possible Damaged Zones in Beam Structures", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 33, 2015. doi:10.4203/ccp.109.33
Keywords: damage detection, non-dominated sorting genetic-II, multi-objective optimization, dynamic parameters.

Summary
In the scientific literature, many vibration-based damage detection methodologies use artificial neural networks (ANNs). The majority of these methodologies are designed to determine the existence of damage in structures (or lack thereof). However, when it comes to locating and quantifying damage in large structures, ANNs are hindered by the large number of training cases needed to guarantee proper damage detection. This paper proposes a perceptron multi-layer neural network to locate damage zones in a beam structure, working from the assumption that such identification represents a step to be done prior to damage quantification. The input data vector was based on the modal flexibility matrix, and the output vector indicated whether a specific zone in the structure might be damaged. Only a few initial modes were measured at a specific quantity of points on the beam. A post-processing computation was then employed to improve the ANN results. A confidence level was achieved in terms of expected damage zones. If 50 percent of the zones were considered to be "probably damaged", the methodology turned out to be approximately 90 percent successful. Yet, this reliability may have been affected by the damage extent of the damaged elements. The use of free-of-noise measurements led to an identification level close to 100 percent. In summary, the results point to the proposed methodology's ability to detect damage as highly prejudiced by incompleteness and noise (in the measurements).

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description