Modelling Railway Bridge Degradation Based on Historical Maintenance Data

Abstract

As structure deteriorates with age and use, it is necessary to devise a maintenance plan to control their states in a cost effective way. In order to evaluate the effectiveness of alternative maintenance strategies their success must be measured by their ability to control the structure condition. The condition can be expressed for either the entire structure or for the components which make up the structure. A problem is how to express this condition. This is a particular problem for bridges where there can be several deterioration mechanisms taking place and there is no clear way of measuring the current state of either the structure of its elements. One approach to defining the condition of bridges is to use condition scores or condition indices, for the infrastructure owners, it is it is desirable that they understand how their population of assets is changing over time. For bridges this has involved providing a condition rating for each structure based on observation and by tracking the changes in the distribution of structure condition for population over time. The current maintenance strategy can then be shown to be inadequate (leading to deteriorating population condition), adequate (producing a stable population condition) or effective and resulting in an improving population condition.There have been a variety of bridge condition scoring systems that have been devised by different infrastructure owners in both the highway and railway sectors. Whilst these scores are not devised to be used in detailed maintenance modelling, due to the lack of alternative data they have frequently been used in this manner. This paper addresses the problems of using this data for bridge degradation modelling and proposes an alternative method to model the degradation of bridge elements using historical work done data. The deterioration process is modelled by a Weibull distribution that governs the time a component deteriorates to a degraded condition state following a repair. The method is demonstrated on real historical maintenance data where the analyses of the deterioration processes of several bridge main bridge components are presented.

A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

“…The transition probabilities were calculated based on the median duration years. Le and Andrews (2015) modeled the deterioration of the bridge elements based on the twoparameter weibull distribution. Anderson Darling test is used to compare between a group of probability distributions.…”

A Petri net model for railway bridge maintenance

“…The detail of the lifetime analysis is discussed in the work by Le and Andrews. 16,21 Having obtained the lifetime data for bridge components, components of the same type and materials have been grouped together and, assuming the data to come from a homogeneous sample, a two-parameter Weibull distribution used to model the time to deteriorate to the specified defect characteristic of that particular element. For the two-parameter Weibull distribution, the expression for the probability distribution function (pdf) is…”

Bayesian network models for making maintenance decisions from data and expert judgment

“…To model the deterioration of assets through several states of repair, we adopt the grading system used in Le & Andrews (2015), where the condition of assets is classified as: New, Good, Poor and Very Poor. By using the model in section 3.1, we can extend it with multiple states (Figure 3), with transition ages from New to Good (Transition 1), Good to Poor (Transition 2), and Poor to Very Poor (Transition 3), each modeled by Weibull distributions.…”

“…In contrast with the assumption made in Le & Andrews (2015), we do not assume maintenance can restore the asset back to its new condition completely, which would be more practical in real life applications. Four types of maintenance scenarios are modelled: no actions, minor intervention, major intervention and renewal.…”