Reliability Prediction of Bridges Based on Monitored Data and Bayesian Dynamic Models

Abstract:

Bridges are subjected to time-dependent loading and strength degradation processes. The main purposes of the designers and the owners are to keep these processes under control, to real-timely know and predict the structural time-variant reliability indices through health monitoring for bridge structures. The sensors of monitoring systems used in structural engineering provide data used for reliability prediction. But how to make use of monitored data to predict and make assessment of the time-variant reliability indices of bridges has become the bottleneck in the field of structural health monitoring (SHM). Bayesian dynamic models can combine the structural monitoring information with the structural reliability, and also can consider the uncertainty of the mass monitoring information. Therefore, in this paper firstly the bayesian dynamic model is built based on the monitoring information; secondly the monitoring mechanism of the monitoring information is given based on the built bayesian dynamic model; thirdly structural reliability indices are predicted based on the monitoring information and the built bayesian dynamic models; finally an actual example is provided to illustrate the feasibility and application of the built bayesian dynamic models in this paper.

• Periodical: Key Engineering Materials (Volume 574)
• Pages: 77-84
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.574.77
• Online since: September 2013
• Authors: Xue Ping Fan, Da Gang Lu
• Keywords: Bayesian Dynamic Models, Bridge, Structural Health Monitoring (SHM), Time-Dependent Reliability Prediction

References

[1] D.M. Frangopol, A. Strauss, and S. Kim. Use of monitoring extreme data for the performance prediction of structures: General approach. Engineering Structures, 30: 3644-3653. (2008)

DOI: 10.1016/j.engstruct.2008.06.010

[2] D.M. Frangopol, A. Strauss, and S. Kim. Bridge reliability assessment based on monitoring. Journal of Bridge Engineering, ASCE, 13(3): 258-270. (2008)

DOI: 10.1061/(asce)1084-0702(2008)13:3(258)

[3] A. Strauss, D.M. Frangopol, and S. Kim. Use of monitoring extreme data for the performance prediction of structures: Bayesian Updating. Engineering Structures, 30: 3654-3666. (2008)

DOI: 10.1016/j.engstruct.2008.06.009

[4] A. H.-S. Ang, W.H. Tang. Probability concepts in engineering planning and design, vol. II. Wiley, New York. (1984)

[5] X.P. Fan, D.G. Lu. Bayesian prediction of structural bearing capacity of aging bridges based on dynamic linear model. Journal of Harbin Institute of Technology, 44(12): 13-17. (2012) (in Chinese)

[6] X.P. Fan, D.G. Lu. Real-time reliability forecast of bridge structures based on multiple Bayesian dynamic linear models. Journal of South China University of Technology, 41(3). (2013) (in Chinese)

[7] X.P. Fan. Time-dependent reliability assessment of concrete continuous beam bridge based on real-time monitoring information. Harbin: Harbin Institute of technology. (2010) (in Chinese)

[8] D.G. Lu, X.P. Fan. Bayesian forecasting of structural bending capacity of aging bridges based on dynamic linear model. Life-Cycle and Sustainability of Civil Infrastructure Systems- Proceedings of the 3rd International Symposium on Life-Cycle Civil Engineering, (IALCCE 2012), October 3-6, Vienna, Austria, 268-274. (2012)

DOI: 10.1201/b12995-8

[9] M. West, J. Harrison. Bayesian forecasting and dynamic models (Second Edition). Springer series in statistics. (1997)

[10] G. Petris, S. Petrone, P. Campagnoli. Dynamic linear models with R. New York: Springer series. (2009)

[11] H.N. Mahmoud, R.J. Connor, C.A. Bowman. Results of the fatigue evaluation and field monitoring of the I-39 Northbound Bridge over the Wisconsin River. ATLSS report no. 05-04, Bethlehem (PA, USA): Lehigh University. (2005)