Nonparametric Structural Damage Detection Algorithm for Ambient Vibration Response: Utilizing Artificial Neural Networks and Self-Organizing Maps
Publication: Journal of Architectural Engineering
Volume 22, Issue 2
Abstract
This study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.
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Acknowledgments
The financial support for this research was provided by Qatar National Research Fund (QNRF; a member of the Qatar Foundation) via the National Priorities Research Program (NPRP), Project Number NPRP 6-526-2-218. The statements made herein are solely the responsibility of the authors.
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Published In
Journal of Architectural Engineering
Volume 22 • Issue 2 • June 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: May 29, 2015
Accepted: Nov 18, 2015
Published online: Feb 19, 2016
Published in print: Jun 1, 2016
Discussion open until: Jul 19, 2016
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