Predictive Analysis by Incorporating Uncertainty through a Family of Models Calibrated with Structural Health-Monitoring Data
Publication: Journal of Engineering Mechanics
Volume 139, Issue 6
Abstract
Complex analysis and design of structures, especially landmark structures such as long-span bridges, have been conducted by many engineers and researchers. Currently, it is possible to collect more and precise monitoring data as well as to develop complex three-dimensional (3D) FEM models. These models, which can be calibrated using structural health-monitoring (SHM) data, can be used for the estimation of component and system reliability of bridges. However, the uncertainties related to the data, analysis, and nonstationary nature of the structural behavior need to be better incorporated by using a set of models that are continuously updated with monitoring data. This set of models constitutes a family as a result of the approach by which the models are obtained and the relationships among them. The objective of this paper is to explore the impact of uncertainty in predicting the system reliability obtained by a one-time, initially calibrated FEM model as well as by a family of FEM models continuously calibrated with monitoring data. To explore the uncertainty effects, a laboratory structure that has a combined system configuration with main and secondary elements is monitored. The monitoring data are employed for the FEM model calibration by using artificial neural networks (ANNs) to obtain parent (calibrated) FEM models from which a set of offspring FEM models is generated to incorporate the uncertainties. It is shown that the use of parent-offspring FEM models becomes important especially when critical parameters that have an impact on the model responses cannot be precisely defined. Finally, it is shown in a comparative fashion that the prediction of reliability using a family of FEM models and a single model can be quite different because the family of models provides a more realistic estimate of the structural responses and probability of failure.
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Acknowledgments
The research project described in this paper was supported by the Federal Highway Administration (FHWA) Cooperative Agreement Award No. DTFH61-07-H-00040. The authors would like to express their profound gratitude to Dr. Hamid Ghasemi of the FHWA for his support of this research. The authors also would like to acknowledge the contributions of their research collaborators and their research team. Dr. Mustafa Gul, Dr. Yunus Dere, and Mr. Taha Dumlupinar were instrumental in the laboratory test and numerical studies. The authors would also like to acknowledge the review and constructive feedback of the anonymous reviewers. Their review greatly helped in revising and improving the paper. The opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organization.
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Published In
Journal of Engineering Mechanics
Volume 139 • Issue 6 • June 2013
Pages: 712 - 723
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Aug 30, 2010
Accepted: Sep 29, 2011
Published online: Oct 3, 2011
Published in print: Jun 1, 2013
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