Maintenance strategy optimisation for infrastructure assets through cost modelling

In infrastructure asset management, maintenance strategies in terms of cost modelling is normally adopted to achieve two broad strategic objectives: to ensure that sufficient funding is available to maintain the portfolio of assets; and to ensure that a minimum cost is achieved while maintaining safety. The data and information required for carrying out cost modelling are often not sufficient in quantity and quality. Even if the data is available, the uncertainty associated with the data and the assessment of the assets’ condition remain a challenge to be dealt with. We report in this paper that cost modelling can be carried out at the initial stage instead of delaying it due to data insufficiency. Subjective experts’ knowledge is elicited and utilised together with some information which is gathered only for a small sample of assets. Linear Bayes methods is adopted to combine the sample data with the subjective experts’ knowledge to estimate unknown model parameters of the cost model. We use a case study from the rail industry to demonstrate the methods proposed in this paper. The assets are metal girders on bridges from a rail company. The optimal maintenance strategy is obtained via simulation based on estimated model parameters