Abstract
This paper presents a preliminary study on developing a new algorithm for making inference on the unsupervised structural health monitoring problems in a Bayesian framework. The main constraint in such problems, besides their unsupervised nature, is the small size of data set. Secondly, most often, there is no numerical model or enough empirical data for computing an appropriate prior density for Bayesian data analysis. Gaussian Mixture Model (GMM) and Kernel Density Estimate (KDE) are the main tools which are used in this paper for density estimation under the constraint on the size of data sets. To solve the second issue, an empirical Bayesian approach is employed for computing a prior density without any model; therefore, this algorithm provides an approximation to the standard Bayesian inference technique. An important aspect of the proposed algorithm is that it provides posterior probabilities for the intact or damaged states of the structure. Such results can be directly used for cost analysis and decision making in such unsupervised problems. The efficacy of the algorithm is experimentally verified by testing a three-story two-bay steel laboratory structure. The results show that the algorithm can effectively detect and localize the damages.
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Acknowledgment
The authors acknowledge the support provided by Royal Dutch Shell through the MIT Energy Initiative, and thank chief scientist Dr. Sergio Kapusta, project managers Dr. Keng Yap and Dr. Yile Li, and Shell-MIT liaison Dr. Jonathan Kane for their oversight of this work. Also, thanks are due to Dr. Michael Feng and his team from Draper Laboratory for their collaboration in the development of the laboratory structural model and sensor systems.
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© 2015 The Society for Experimental Mechanics, Inc.
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Mohammadi-Ghazi, R., Buyukozturk, O. (2015). Bayesian Inference for Damage Detection in Unsupervised Structural Health Monitoring. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15224-0_30
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DOI: https://doi.org/10.1007/978-3-319-15224-0_30
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