Large-Scale Hybrid Bayesian Network for Traffic Load Modeling from Weigh-in-Motion System Data
Publication: Journal of Bridge Engineering
Volume 20, Issue 1
Abstract
Traffic load plays an important role not only in the design of new bridges but also in the reliability assessment of existing structures. Weigh-in-motion systems are used to collect data to determine traffic loads. In this paper, the potential of hybrid nonparametric Bayesian networks (BNs) is demonstrated for modeling the complex data measured by the weigh-in-motion systems. The quantification process provides insight into the statistical buildup of the traffic load. The BN is shown to be a reliable traffic load model for use in bridge design. The model’s value is shown with applications for prediction of missing data and calculation of extreme loads. A simulation that includes both a dynamic BN and a static component is performed. The model is able to generate the distribution function of section forces, such as bending moments, generated by multiple vehicles in several lanes. The model presented in this paper should serve as a benchmark for further applications.
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Acknowledgments
This research has been partially financed by Netherlands Organization for Applied Scientific Research (TNO) under the Graphical Models for Systems Risk and Reliability project. The authors are grateful to Mariem Zouch for her contributions to this paper.
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Published In
Journal of Bridge Engineering
Volume 20 • Issue 1 • January 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 1, 2012
Accepted: Apr 7, 2014
Published online: May 14, 2014
Published in print: Jan 1, 2015
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