Optimum preventative maintenance strategies using genetic algorithms and Bayesian updating

Abstract

Preventative maintenance (PM) includes proactive maintenance actions that aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds because it can extend the life of bridges and avoid the need for unplanned essential maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed an optimisation methodology based on genetic algorithm (GA) principles, which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. To further improve the reliability of estimating the degree of deterioration of an element, which is a key element in predicting optimum PM strategies using the GA methodology, Bayesian updating is utilised. The use of Bayesian updating enables the updating of the probability of failure based on data from site inspection or laboratory experiments and the adjustment, if necessary, of the timing of subsequent PM interventions. For the case study presented in this paper, the probability of failure is expressed as the probability of corrosion initiation of a reinforced concrete element due to de-icing salt.