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In comparison with the well‐researched field of analysis and design of structural systems, the life‐cycle performance prediction of these systems under no maintenance as well as under various maintenance scenarios is far more complex, and is a rapidly emergent field in structural engineering. As structures become older and maintenance costs become higher, different agencies and administrations in charge of civil infrastructure systems are facing challenges related to the implementation of structure maintenance and management systems based on life‐cycle cost considerations. This article reviews the research to date related to probabilistic models for maintaining and optimizing the life‐cycle performance of deteriorating structures and formulates future directions in this field.

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Reader AidsPurpose: Widen state of the art Special math needed for explanation: Probability, statistics Special math needed to use results: Same Results useful to: Reliability theoreticians, maintenance analysts Summary t Conclusions -This paper proposes a comprehensive method for the use of expert opinion for obtaining lifetime distributions required for maintenance optimization. The method includes procedures for the elicitation of discretized lifetime distributions from several experts, the combination of the elicited expert opinion into a consensus distribution, and the updating of the consensus distribution with failure and maintenance data. The method was motivated by the practical circumstances governing its implementation. In particular, by the lack of statistical training of the experts and the high demands on their time. The use of a discretized life distribution provides more flexibility, is more comprehendible by the experts in the elicitation stage, and greatly reduces the computation in the combination and updating stages. The methodology is Bayes, using the Dirichlet distribution as the prior distribution for the elicited discrete lifetime distribution. Methods are described for incorporating information concerning the expertise of the experts into the analysis.

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