Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 2
Abstract
This paper presents a novel Gibbs sampling approach for structural health monitoring (SHM) with detection of structural changes/damages using incomplete complex modal data measured with a limited number of sensors. The usual difficulty with the availability of sensors in SHM practices and enforcing data acquisition in multiple setups is thoroughly addressed. Structural modeling incorporated with damping is considered in this proposed inverse problem exercise to calibrate damping parameters along with the stiffness and mass parameters facilitating SHM. Both proportional and nonproportional viscous damping are adopted in structural modeling. Detailed formulations on the probabilistic detection of changes/damages are presented in detail. Moreover, a Gibbs sampling technique is introduced to quantify uncertainties of the various sets of uncertain parameters, where samples of the conditional probability density function of a parameter set are obtained iteratively. The proposed approach retains the typical advantage of the nonrequirement of mode-matching. A validation exercise is performed using a three-dimensional building structure (attached with supplementary viscous dampers) and a laboratory steel structure considering multiple damage cases and different sensor placements. The proposed methodology is observed to be efficient for SHM using incomplete complex modal data measured with a limited number of sensors.
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Data Availability Statement
Some or all of the data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Except for the data and model, the code (developed in-house) implementing the proposed methodology is proprietary. However, the code can be shared with the restriction that the shared code cannot be distributed further and will be used only for review purposes.
Acknowledgments
The authors acknowledge the financial support from the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (Project file No. YSS/2015/001224).
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 2 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 1, 2020
Accepted: Dec 31, 2020
Published online: Mar 10, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 10, 2021
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