ABSTRACT
The behaviour of the coating system of composite bridges over time is an important issue as it has significant implications in terms of costs and environmental impacts. However, the estimation of the service life of the bridge and, in particular, of its coating protection usually relies on the information provided by the suppliers and/or experience of the engineers, as life cycle approaches are not yet a daily practice among bridge engineers. The aim of this paper is to assess the time-variant performance of the protective coating of composite bridges and respective impacts. An available simplified tool was adopted in this work, which enables to model the coating degradation of steel bridges and correlate this with the condition of the bridge (or bridge element) (MainLine, 2014). However, the available tool has some major disadvantages, since it does not enable the consideration of uncertainties in the main parameters of the model nor the optimization of the strategy leading to the minimization of costs or other constraints. To cope with these limitations, the tool was enhanced by the consideration of an optimization procedure that is able to cope with uncertainties.
The advantages of the enhanced tool are illustrated in a case study consisting of a composite steel-concrete bridge overpassing a dual carriageway. The bridge has three spans: the side spans are 18.5 m and the inner span is 40.8 m long. The composite deck is represented in Figure 1. Cross-section of the composite bridge. https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315207681/cd556cd4-4dcf-4efe-8e29-56fc67b8bfbd/content/fig332_1.tif"/>
For the probabilistic analysis, two interventions strategies were enable: repainting of the element and/or strengthening of the element; and 3 corrosivity categories were considered: C3, C4 and C5. The results are indicated in Table 1 for the accumulated present value. The analysis was performed by Monte Carlo simulation, assuming 1000 iterations and 100 simulations. Comparison of the main results of the probabilistic analysis.
Corr. Cat
Mean
Std Dev
Min
Max
C3
372 k€
88 k€
220 k€
617 k€
C4
516 k€
120 k€
324 k€
822 k€
C5
1153 k€
765 k€
577 k€
2085 k€
The higher the corrosivity category, the higher is the scatter of values and thus, the higher is the mean value of the cost in relation to the deterministic analysis.
Then an optimization analysis was performed to find the target values for the two interventions strategies and the results are indicated in Table 2. Comparison of the main results of the optimization analysis.
Corr. Cat
Mean
Std Dev
Min
Max
C3
273 k€
58 k€
150 k€
411 k€
C4
380 k€
66 k€
238 k€
545 k€
C5
727 k€
178 k€
461 k€
1244 k€
As expected, the mean values have been reduced significantly in relation to the previous analysis
