OPTIMUM PREVENTATIVE MAINTENANCE STRATEGIES USING GENETIC ALGORITHM AND BAYESIAN UPDATING

Abstract eng:
Preventative maintenance (PM) includes proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge 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. Here the use of Bayesian updating enables the updating of the probability of failure based on site inspection / laboratory data and the adjustment if necessary of the timing of subsequent PM interventions. In the case study presented in this paper the probability of failure is the probability of corrosion initiation of a reinforced concrete element due to de-icing salt.

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