Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

Barbara Heitner

BSc, MSc
Home/Barbara Heitner
Barbara Heitner 2017-07-01T20:23:46+00:00
Early Stage Researcher
Phimeca Engineering (France)

Project 8: Probabilistic modelling of bridge damage based on damage indicators

Click on the icons for Barbara’s email, blog and Linkedin profile

Research Interests:

Structural analysis; Life-cycle analysis of bridges; Structural health monitoring of bridges; Probabilistic modeling; Risk analysis


Barbara graduated from the Faculty of Civil Engineering at the Budapest University of Technology and Economics in 2013. During her Bachelor studies, she spent one year in Prague at the Czech Technical University as an exchange student.

In the summer of 2012 she did a traineeship at the company Eurovia in the Czech Republic.

From September 2013 till February 2015 she was enrolled to the Erasmus Mundus Joint Master course “SUSCOS” – Sustainable Constructions under Natural Hazards and Catastrophic Events. During the 18 months of the program she followed courses in Belgium and Romania, while she wrote her Master Thesis in Portugal. She joined TRUSS ITN in September 2015.

Research Outputs:

Publications in TRUSS

This paper introduces the various aspects of bridge safety models. It combines the different models of load and resistance involving both deterministic and stochastic variables. The actual safety, i.e. the probability of failure, is calculated using Monte Carlo simulation and accounting for localized damage of the bridge. A possible damage indicator is also presented in the paper and the usefulness of updating the developed bridge safety model, with regards to the damage indicator, is examined. -> Link to publisher’s version  -> Link to full text in repository

DOI: http://dx.doi.org/10.1016/j.proeng.2016.08.279

 Probabilistic assessment of ageing bridges has become an important research area as it interests not only researchers but investors, municipalities and even governments. In this paper a simple bridge model is presented in a probabilistic context. A comparative study is carried out involving damage indicators and Bayesian updating. Bayesian updating is a powerful tool, which has been used in various research areas. However, using it for approximating the safety level of a bridge is challenging due to the various sources of uncertainties that may affect the performance of a measurement based damage indicator. The effects of different factors involved in the updating are examined in this paper and compared. 
Probabilistic assessment of bridges has been the subject of various studies in recent decades. It has been widely agreed that evaluating an existing bridge according to the standards and codes used for new structures can lead to demolition of a safe bridge or unnecessary repairs, and thus to high economic cost and an increase in the associated environmental impact. This paper investigates several concerns, the sensitivities of and correlation between the different stochastic parameters influencing the load on a bridge and its resistance to that load. The usefulness of updating the bridge safety model using damage indicators from a Structural Health Monitoring system is also examined.

The proposed approach combines a number of aspects. Firstly, a probabilistic bridge load model is established based on Weigh-In-Motion (WIM) data to mimic a realistic traffic flow and hence, the loads and their effects on the bridge. Traffic loading is highly correlated as the same vehicles influence many parts of the bridge. This has a significant influence on the probability of failure.

To model the resistance of the bridge a probabilistic approach is used and full correlation between segments is assumed. Combining the load and resistance models, the probability of failure can be inferred. In the future work the bridge safety model, more precisely the resistance model, will be updated. Bayesian updating will be used in the current framework based on the information obtained from specific damage indicators.

This study aims at obtaining valuable information regarding the importance of the different aspects of bridge safety models and the sensitivity of the probability of failure (i.e. the level of safety) to them. It is also expected to confirm the applicability of a Bayesian approach to this problem. -> Link to full text in repository