Mr. Amit Chandra Profile

Amit Chandra

Doctoral Candidate

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Proceedings Article | 27 March 2018 Presentation + Paper

System identification and vibration-based damage detection in a concrete shear wall system

A. Soltani, A. Sabamehr, A. Chandra, A. Bagchi

Proceedings Volume 10598, 105982X (2018) https://doi.org/10.1117/12.2296623

KEYWORDS: Neural networks, System identification, Damage detection, Earthquakes, Structural health monitoring, Matrices, Civil engineering

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Structural Health Monitoring (SHM) based on the vibration of structures has been very attractive subject for researchers in different fields such as: civil, aeronautical and mechanical engineering. System Identification (SI) and Vibration based Damage Identification (VBDI) are two main parts of SHM. A full-scale seven-story reinforced concrete (RC) wall building has been tested during October 2005 and January 2006 by the University of California at San Diego (UCSD). The building was excited through four historical California ground motions. The RC wall experienced different levels of damage, progressively under increasing intensity of ground motions. At different levels of damage, the building was subjected to ambient vibration tests and low-amplitude White Gaussian Noise (WGN) base excitation. In this study, the response of the structure to ambient vibration tests is used to identify damage using VBDD method. The frequency domain decomposition method (FDD) is used to identify the modal parameters of the building. Damage changes the modal properties (frequency, mode shape and damping) by reducing the stiffness. Therefore, changes in the vibration characteristics of the structure can be used to identify location and severity of damage. A mode shape curvature-based method is used to detect and localize damage. Also a data-driven technique based on Neural Networks has been developed to identify the damage in the structure. The results show a close correlation with the structural damage observed in the experimental study.

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