Maintenance decision model for steel bridges: a case in the Netherlands

Abstract

A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.

A Bayesian Belief Network method for bridge deterioration detection

“…The CPTs are hereafter defined by considering that the degradation of a bridge can be modelled by adopting a linear model. 36–38 Assume that a bridge element, described by node X , is in state x i . There are N parent nodes of this element, denoted as a set Y k .…”

“…where y pol is the displacement of a bridge element when the noise is added, y FE is the displacement value provided by the FEM, q p is the ratio of standard deviation between the noise and the FEM displacement y FE and is equal to 5%. 37 N is a standard normal distribution of mean 0 and standard deviation 1, and σ ( y FE ) is the standard deviation of the displacement of the FEM elements when the same health state of the bridge is simulated by modifying environmental factors, such as the load on the bridge.…”

“…h) Compute the interdependencies between bridge elements. The CPTs are hereafter defined by considering that the degradation of a bridge can be modelled by adopting a linear model [Kreislova and Geiplova, 2012;Attema et al, 2017;Rao et al, 2017]. Assume that a bridge element, described by node X, is in state xi.…”

“…to 5%[Attema et al, 2017]. N is a standard normal distribution of mean 0 and standard deviation 1, and ( )FE y is the standard deviation of the displacement of the FEM elements when the same health state of the bridge is simulated by modifying environmental factors, such as the load on the bridge.…”

Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges

“…162 The FE model can simulate the behaviour of each element of the bridge, and therefore, the influence among different degrading elements of the bridge can be assessed. Furthermore, the FE model can also predict the future behaviour of the bridge, if it is coupled with a mathematical model that describes the evolution of the degradation of the bridge material over time, such as the growth of cracks 166,167 or the evolution of the corrosion. 168,169…”