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Optimum preventative maintenance strategies using genetic algorithms and Bayesian updating

机译：使用遗传算法和贝叶斯更新的最佳预防性维护策略

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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.
机译：预防性维护（PM）包括旨在防止或延迟可能导致故障的恶化过程的主动维护措施。可以从经济角度考虑这种维护，因为它可以延长桥梁的使用寿命，并避免了计划外的必要维护。由于PM措施的有效集成在桥梁维护策略中的重要性，因此作者开发了一种基于遗传算法（GA）原理的优化方法，该方法将各种PM措施的概率有效性与其成本联系起来，以便制定最佳的PM策略。为了进一步提高估计元素变质程度的可靠性，这是使用GA方法预测最佳PM策略的关键元素，因此采用了贝叶斯更新。贝叶斯更新的使用使得能够基于现场检查或实验室实验的数据来更新故障概率，并在必要时调整后续PM干预的时间。对于本文中介绍的案例研究，破坏的可能性表示为由于除冰盐而引起的钢筋混凝土构件腐蚀开始的可能性。

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