Robustness‐based performance assessment of a prestressed concrete bridge

Life-cycle civil engineering addresses, among other things, the growing number of deteriorating bridges and the associated economic challenges. As a consequence, government bodies, infrastructure and bridge owners as well as industry request objective and rational performance indicators for classification and intervention planning in structural engineering. This paper focuses on a methodology for analysing the damage-based robustness margins of bridge systems under traffic loading. In particular, a series of emergent deterioration-based damage scenarios are compared with the actual or virgin state in terms of loadbearing capacity and serviceability. Non-linear finite element analysis based on a detailed 3D model has a high potential for capturing the available bridge capacity for different degradation phenomena and levels, serving as an input for further reliability-based performance indicators. Notwithstanding, costs associated with fully probabilistic assessment measures are still prohibitive despite technological advances and new methods of reducing the sample size in Monte Carlo computations. In addition, considering the large uncertainties and imprecision involved, it is imperative that probabilistic schemes are preferred over deterministic assessments. The objective of this article is to present strategies for robustness-based performance assessment using non-linear modelling and to discuss relevant reliability-based quantities and performance indicators in relation to structural damage using the example of specific degradation events in an existing prestressed box girder bridge. Furthermore, some strategies are developed on the basis of the new approach for general complex engineering structures.

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

[2]  Marc A. Maes,et al.  Structural Robustness in the Light of Risk and Consequence Analysis , 2006 .

[3]  Vladimir Cervenka,et al.  Reliability‐based non‐linear analysis according to fib Model Code 2010 , 2013 .

[4]  Dan M. Frangopol,et al.  Assessment of Existing Structures Based on Identification , 2010 .

[5]  Dan M. Frangopol,et al.  Effects of Damage and Redundancy on Structural Reliability , 1987 .

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

[7]  Pryl Dobromil,et al.  Material model for finite element modelling of fatigue crack growth in concrete , 2010 .

[8]  Konrad Bergmeister,et al.  Numerically and Experimentally Based Reliability Assessment of a Concrete Bridge Subjected to Chloride-Induced Deterioration , 2013 .

[9]  Dan M. Frangopol,et al.  A probabilistic approach for the prediction of seismic resilience of bridges , 2013 .

[10]  Konrad Bergmeister,et al.  Stochastische Parameteridentifikation bei Konstruktionsbeton für die Betonerhaltung , 2004 .

[11]  Mark G. Stewart,et al.  Spatial time-dependent reliability analysis of corrosion damage and the timing of first repair for RC structures , 2007 .

[12]  Bretislav Teplý,et al.  Reinforcement Corrosion: Limit States, Reliability and Modelling , 2012 .

[13]  Libor Jendele,et al.  On the solution of multi-point constraints - Application to FE analysis of reinforced concrete structures , 2009 .

[14]  Jan Podroužek,et al.  Modelling of Chloride Transport in Concrete by Cellular Automata , 2008 .