MATEC Web Conf.
Volume 211, 2018The 14th International Conference on Vibration Engineering and Technology of Machinery (VETOMAC XIV)
|Number of page(s)||6|
|Section||TP12: Structural health monitoring|
|Published online||10 October 2018|
An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower
ABC Department, Politecnico di Milano,
2 Department of Applied Computational Mechanics, Juiz de Fora, Brazil
* Corresponding author: mailto:firstname.lastname@example.org
Automated modal identification procedures are attracting the interest of the Structural Health Monitoring (SHM) community as those techniques are capable of continuously providing information which are useful to timely assess the health state of a structure. Within this context, the paper presents the development and application of a vibration-based novelty detection strategy using automatically identified resonant frequencies and the Support Vector Machine (SVM) approach. The SVM is a popular technique for forming decision boundaries that separate data into two or more classes without any prior assumptions on the propriety of the data. The developed procedure is exemplified using frequency data collected during the continuous dynamic monitoring of a historic masonry tower that underwent slight permanent variation of the natural frequencies after the occurrence of a far-field earthquake. The obtained results highlight the capability of the novelty strategy to reveal slight damage and to detect anomalies in the structural behaviour.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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