Structural Health Monitoring via Phase Space Warping and Time-Delay Embedding
Publication: Journal of Structural Engineering
Volume 149, Issue 2
Abstract
The state of a structure’s health can be determined by investigating its vibration characteristics. Vibration-based structural health monitoring (SHM) enables early detection and diagnosis of damage as well as extension of service life. Herein, linear and nonlinear tracking metrics are proposed to track deterioration in the condition of multistory structures and assess their instantaneous health in real-time via measurement of floor accelerations. The linear metrics, the amplitude damage index and the frequency damage index , are based on tracking the power spectra of floor accelerations. The nonlinear metric is based on a novel implementation of the phase-space warping method and obtained from the orbits representing the floors’ motions in pseudo phase-space. A scaled-down model of a four-floor moment-resisting frame building is designed and fabricated to demonstrate and to compare the capabilities of the three damage indices. Structural damage is introduced to individual columns, to mimic damage initiation, by cutting two notches on opposite sides of a column cross-section at midheight. The detected large damage events, fast deterioration beyond them, and the onset of failure using any of the four floor accelerations. The proved insensitive to damage compared with the other two metrics. The nonlinear metric detected gradual (fatigue-induced) deterioration in the building’s health before introduction of damage, large damage events, fast deterioration beyond them, and the onset of failure, using any of the four floor accelerations. The metric varied slowly and continuously with gradual deterioration and exhibited larger discontinuous jumps with discrete damage events. This was true for all three damage experiments undertaken on the model building. The nonlinear metric was also found to be more efficient, in terms of signal utilization, in comparison with the linear metric.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was financially supported, in part, by the American University of Sharjah (AUS) through the faculty research grant program (FRG19-M-E65). The financial support is greatly appreciated by the fourth author, Professor Mohammad AlHamaydeh. This paper represents the opinions of the authors and does not mean to represent the position or opinions of AUS.
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© 2022 American Society of Civil Engineers.
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Received: Feb 17, 2022
Accepted: Oct 10, 2022
Published online: Nov 29, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 29, 2023
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