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Non-contact modal parameters identification using a K-cluster algorithm
Author(s): Marco Torbol; Amir Nasrollahi; Piervincenzo Rizzo
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Paper Abstract

Non-contact structural health monitoring is a promising field for assessing civil structures, such as bridges. Not having to access the structure avoids different issues: the closure of the structure, the use of special equipment to access it, and others. This study uses digital image processing, machine learning, and parallel computing to detect the vibration of a flexible structure. If a structure is too stiff, a reinforced concrete short-span bridge or a multi-story building, it is hard to identify its natural frequencies without some sort of target panel or target feature. Instead, if the structure is flexible, it is possible to identify its displacement and its natural frequencies, but it is a challenge with high computational cost. This study presents an unsupervised machine-learning algorithm to identify a structure, its displacement, and its natural frequencies. The algorithm was deployed on a simple supported beam using a commercially available camera and an inexpensive GPU.

Paper Details

Date Published: 27 March 2019
PDF: 7 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 1097004 (27 March 2019); doi: 10.1117/12.2514443
Show Author Affiliations
Marco Torbol, Ulsan National Institute of Science and Technology (Korea, Republic of)
Amir Nasrollahi, Univ. of Pittsburgh (United States)
Piervincenzo Rizzo, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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