Reliability assessment of cable-stayed bridges based on structural health monitoring techniques

This paper presents the reliability analysis approach of long-span cable-stayed bridges based on structural health monitoring (SHM) technology. First, the framework of structural reliability analysis is recognised based on SHM. The modelling approach of vehicle loads and environmental actions and the extreme value of responses based on SHM are proposed, and then models of vehicle and environmental actions and the extreme value of inner force are statistically obtained using the monitored data of a cable-stayed bridge. For the components without FBG strain sensors, the effects and models (extreme values) of dead load, unit temperature load, and wind load of the bridge can be calculated by the updated finite element model and monitored load models. The bearing capacity of a deteriorated structure can be obtained by the updated finite element model or durability analysis. The reliability index of the bridge's critical components (stiffening girder in this study) can be estimated by using a reliability analysis method, e.g. first order reliability method (FORM) based on the models of extreme value of response and ultimate capacity of the structure. Finally, the proposed approach is validated by a practical long-span cable-stayed bridge with the SHM system. In the example, reliability indices of the bridge's stiffening girder at the stage after repair and replacement after 18 years of operation, and the damaged stage are evaluated.

[1]  Yan-Gang Zhao,et al.  Second-Order Third-Moment Reliability Method , 2002 .

[2]  Hui Li,et al.  Modal identification of bridges under varying environmental conditions: Temperature and wind effects , 2009 .

[3]  Renzo Rosso,et al.  Statistics, Probability and Reliability for Civil and Environmental Engineers , 1997 .

[4]  Dan M. Frangopol,et al.  Cost-Reliability Interaction in Life-Cycle Cost Optimization of Deteriorating Structures , 2004 .

[5]  Johannes O. Royset,et al.  Extensions of Turkstra's rule and their application to combination of dependent load effects , 2000 .

[6]  Tianjun Miao.  Bridge live load models with special reference to Hong Kong , 2001 .

[7]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[8]  Olivier Burdet.  Automatic Deflection and Temperature Monitoring of a Balanced Cantilever Concrete Bridge , 1998 .

[9]  Yi-Qing Ni,et al.  Technology developments in structural health monitoring of large-scale bridges , 2005 .

[10]  M. Friswell,et al.  Perturbation methods for the estimation of parameter variability in stochastic model updating , 2008 .

[11]  Dan M. Frangopol,et al.  Probabilistic Lifetime-Oriented Multiobjective Optimization of Bridge Maintenance: Combination of Maintenance Types , 2006 .

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

[13]  Pastsakorn Kitiyodom,et al.  MONITORING OF LOAD DISTRIBUTION OF THE PILES OF A BRIDGE DURING AND AFTER CONSTRUCTION , 2004 .

[14]  Yasuhiro Mori,et al.  Probabilistic models of combinations of stochastic loads for limit state design , 2003 .

[15]  Dan M. Frangopol,et al.  Reliability-Based Assessment of Suspension Bridges: Application to the Innoshima Bridge , 2001 .

[16]  Michael P. Enright,et al.  Condition Prediction of Deteriorating Concrete Bridges Using Bayesian Updating , 1999 .

[17]  Dan M. Frangopol,et al.  Repair Optimization of Highway Bridges Using System Reliability Approach , 1999 .

[18]  N. T. Kottegoda,et al.  Probability, Statistics, and Reliability for Civil and Environmental Engineers , 1997 .