Time-Dependent Reliability Prediction of Bridge Member Based on MGPF and SHM Data

Abstract:

In the long-term monitoring period, the structural health monitoring (SHM) system produces a huge amount of monitoring data. It becomes a very important thing that how to real-timely predict structural reliability indices from such huge number of monitored data. In this paper, To real-timely predict reliability of bridge members with real-time monitoring information, with the long-term mass monitored data of health monitoring system, the data-based dynamic model including observation equation and state equation is built, and then the mixed Gaussian particle filter (MGPF) is introduced. With particle filter method, Bayesian method and dynamic model, the posteriori distribution parameters of state variable and one-step forward prediction distribution parameters of monitored data are predicted. Through resampling technique, with MGPF, the prediction precision of dynamic model can generally increase. Based on the dynamic monitored data, the weights of resampled particles can be constantly updated. Therefore the problem of particle degradation is solved. Finally based on the real-time predicted distribution parameters, with the first order second moment (FOSM) method, the dynamic reliability of bridge member is predicted, and an actual example is provided to illustrate the application and feasibility of the proposed models and methods.

• Periodical: Advanced Engineering Forum (Volume 15)
• Pages: 119-132
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.15.119
• Online since: February 2016
• Authors: Xue Ping Fan*, Yue Fei Liu
• Keywords: Dynamic Model, First Oder Second Moment Method, Mixed Gaussian Particle Filter, Monitored Data, Reliability Prediction

References

[1] Straub, D & Faber, MH. Computational aspects of risk-based inspection planning. Computer-aided Civil and Infrastructure Engineering, 2006, 21 (3): 179-92.

DOI: 10.1111/j.1467-8667.2006.00426.x

[2] Zou YJ. The Role of Structural Health Monitoring in Bridge Assessment and Management. Thesis (PhD). Lehigh University, Bethlehem, PA, USA, (2011).

[3] Li H, Zhou WS, Ou JP, et. al. A study on system integration technique of intelligent monitoring systems for soundness of long-span bridges. China Civil Engineering Journal, 2006, 39(2): 46-52. (in Chinese).

[4] Li AQ, Miu CQ, Li ZX, et. al. Health monitoring system for the Runyang Yangtse River Bridge. Journal of Southeast University, 2003, 33(5): 544-548. (in Chinese).

[5] Qin Q. Health monitoring of long-span bridges. China Journal of Highway and Transport, 2000, 13(2): 37-42(in Chinese).

[6] Chana THT, Yua L &Tam HY. Fiber Bragg Grating Sensors for Structural Health Monitoring of Tsing Ma Bridge: Background and Experimental Observation. Engineering Structures, 2006, 28: 648-659.

DOI: 10.1016/j.engstruct.2005.09.018

[7] Hu XY, Wang B & Ji H. A Wireless Sensor Network-based Structural Health Monitoring System for Highway Bridges. Computer-Aided Civil and Infrastructure Engineering, 2013, 28(3): 193-209.

DOI: 10.1111/j.1467-8667.2012.00781.x

[8] Liu M, Frangopol DM, Kim S. Bridge safety evaluation based on monitored live load effects. Journal of Bridge Engineering, 2009, 14(4): 257-269.

DOI: 10.1061/(asce)1084-0702(2009)14:4(257)

[9] Wang Y, Jing GG, Wang B. On-line Structural Analysis and State Evaluation Method for Bridge Health Monitoring System. Bridge Construction, 2014, 44(1): 25-30.

[10] Ching J, Muto M & Beck JL, (2006). Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler. Computer-Aided Civil and Infrastructure Engineering, 2006, 21(4): 242–257.

DOI: 10.1111/j.1467-8667.2006.00432.x

[11] Xiang J & Liang M. Wavelet-based Detection of Beam Cracks Using Modal Shape and Frequency Measurements. Computer-Aided Civil and Infrastructure Engineering, 2012, 27(6): 439-454.

DOI: 10.1111/j.1467-8667.2012.00760.x

[12] Yuen KV & Katafygiotis LS. Substructure Identification and Health Monitoring Using Response Measurement Only. Computer-Aided Civil and Infrastructure Engineering, 2006, 21(4): 280-291.

DOI: 10.1111/j.1467-8667.2006.00435.x

[13] Fan XP & Lu DG. Real-time reliability forecast of bridge structures based on multiple Bayesian dynamic linear models. Journal of South China University of Technology, 2013, 41(3): 67-72. (in Chinese).

[14] Fan XP & Lu DG. Reliability prediction of aging bridges based on Bayesian dynamic linear model. Journal of the china railway society, 2014a, 36(6), 93-98.

[15] Fan XP & Lu DG. Reliability prediction of bridges based on monitored data and Bayesian dynamic models. Key Engineering Materials, 2014b, 574, 77-84.

DOI: 10.4028/www.scientific.net/kem.574.77

[16] Fan XP & Lu DG. Reliability prediction of bridge structures based on BDNM. Journal of Harbin Institute of Technology, 2014c, 46(2), 1-5.

[17] Zhao Z, (2012). Health Monitoring Data Modeling and Reliability Analysis for Yitong River Bridge based on ARMA Model. Thesis(Master's degree), Harbin Institute of Technology, Harbin, China, 2012. (in Chinese).

[18] Peng YH, Liu YF, Miu D & Yang XG. Application of Gaussian Mixture Particle Filter on State Estimation. DANJIAN YU ZHIDAO XUEBAO, 2007, 27(1): 271-274.

[19] Tang HY. Key Technology of Structural Design and Construction Control of Tianjin Fumin Bridge. Thesis (Master's degree), Tianjin University, Tianjin, China, 2010. (in Chinese).

DOI: 10.29268/stsc.2015.3.4.3

[20] Chen SF & Yang DW. Health Monitoring System Project of Tianjin Fumin Bridge. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China, 2010. (in Chinese).