Use of monitored daily extreme stress data for performance prediction of steel bridges: Dynamic linear models and Gaussian mixed particle filter

Abstract Sensors of modern bridge monitoring systems provide a huge amount of data used for reliability prediction. The proper handling of these data is one of the main difficulties in the field of structural health monitoring. To reasonably predict structural time-variant reliability based on the monitored daily extreme stress data, the objectives of this paper are to present: (a) a procedure for the effective incorporation of monitored daily extreme stress data for dynamic reliability prediction of bridge components, (b) a modelling approach of dynamic linear models based on historical daily extreme stress data in structural reliability prediction, and (c) an effective use of Gaussian mixed particle filter combining the monitored daily extreme stress data and dynamic linear models for dynamically predicting structural reliability. The monitored data obtained from an existing bridge is provided to illustrate the feasibility and application of the procedures and models proposed by this paper.

[1]  Yuefei Liu,et al.  Combinatorial Bayesian Dynamic Linear Models of Bridge Monitored Data and Reliability Prediction , 2016 .

[2]  Yuefei Liu,et al.  Dynamic reliability prediction of bridge member based on Bayesian dynamic nonlinear model and monitored data , 2016 .

[3]  Xue Ping Fan,et al.  Time-Dependent Reliability Prediction of Bridge Member Based on MGPF and SHM Data , 2016 .

[4]  Hong Hao,et al.  Damage detection of shear connectors under moving loads with relative displacement measurements , 2015 .

[5]  Konrad Bergmeister,et al.  Monitoring Information and Probabilistic-Based Prediction Models for the Performance Assessment of Concrete Structures , 2016 .

[6]  Lu Dagang,et al.  Reliability updating and prediction of bridge structures based on proof loads and monitored data , 2014 .

[7]  Jun Li,et al.  Updating the reliability of a concrete bridge structure based on condition assessment with uncertainties , 2010 .

[8]  Dan M. Frangopol,et al.  Use of monitoring extreme data for the performance prediction of structures: Bayesian updating , 2008 .

[9]  Yuefei Liu,et al.  Dynamic reliability prediction of bridges based on nonlinear MGPF algorithm and SHM data , 2017 .

[10]  Giovanni Petris,et al.  Dynamic Linear Models with R , 2009 .

[11]  Dan M. Frangopol,et al.  Reliability-based performance indicators for structural members , 2016 .

[12]  Yingjun Zou.  The Role of Structural Health Monitoring in Bridge Assessment and Management , 2011 .

[13]  Yi-Qing Ni,et al.  Modeling of Stress Spectrum Using Long-Term Monitoring Data and Finite Mixture Distributions , 2012 .

[14]  Xue Ping Fan,et al.  Reliability Prediction of Bridges Based on Monitored Data and Bayesian Dynamic Models , 2013 .

[15]  Da Gang Lu,et al.  Reliability Analysis of Two-Dimensional Series Portal-Framed Bridge System Based on Mixed Copula Functions , 2013 .

[16]  Yi-Qing Ni,et al.  Reliability-based condition assessment of in-service bridges using mixture distribution models , 2012 .

[17]  Dan M. Frangopol,et al.  Use of Monitoring Extreme Data for the Performance Prediction of Structures: General Approach , 2008 .

[18]  Yi-Qing Ni,et al.  Monitoring-Based Fatigue Reliability Assessment of Steel Bridges: Analytical Model and Application , 2010 .

[19]  Dan M. Frangopol,et al.  Structural Health Monitoring and Reliability Estimation: Long Span Truss Bridge Application With Environmental Monitoring Data , 2008 .

[20]  Yi-Qing Ni,et al.  A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment , 2018, Comput. Aided Civ. Infrastructure Eng..

[21]  Jianren Zhang,et al.  Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing , 2014 .

[22]  Hong Hao,et al.  Structural damage identification with power spectral density transmissibility: Numerical and experimental studies , 2015 .

[23]  Songye Zhu,et al.  Long‐term condition assessment of suspenders under traffic loads based on structural monitoring system: Application to the Tsing Ma Bridge , 2012 .

[24]  Xiao Wang,et al.  Comparison of statistical counting methods in SHM-based reliability assessment of bridges , 2015 .