Bayesian Bridge Weigh-in-Motion and Uncertainty Estimation
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 1
Abstract
Many researchers have developed bridge weigh-in-motion (BWIM) technology, mainly focusing on the representative value of the estimated axle weights. However, the estimation of the probabilistic distribution of axle weights is also important for understanding the ill conditioning of BWIM formulations and the uncertainty of estimation. Bayesian updating provides a coherent framework for assimilating data into models. Here, Bayesian bridge weigh-in-motion (BBWIM), which combines Bayesian updating and BWIM, is proposed. BBWIM can estimate not only the representative value of axle weights but also the uncertainty of the estimated value and the correlation among estimates. Uncertainties in estimated axle weight are quantitatively discussed with a simple two-axle problem. It is shown that the estimated weights of closely spaced axles have large uncertainty. BBWIM is applied to the measured data for an actual bridge. It is shown that additional information, in the form of a weak constraint on axle weight, namely, that closely spaced axles have similar weights, can reduce the uncertainty of estimated axle weights.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential and may only be provided with restrictions. If Tokyo City University and Kanto Road Maintenance Management Office agree with the provision of these research resources, these may be provided by the corresponding author. When publishing any research results based on the provided research resources, prior consent must be obtained from the corresponding author. In addition, these data can be used only for research.
Acknowledgments
The collection of field measurement data was supported by Kanto Road Maintenance Management Office and a grant from the Japan Institute of Country-ology and Engineering (JICE) (Grant 17013).
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 1 • March 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 11, 2020
Accepted: Nov 4, 2020
Published online: Jan 5, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 5, 2021
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